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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "75c0974e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
+ "from sklearn.linear_model import LogisticRegression, Ridge\n",
+ "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score, roc_curve, roc_auc_score, auc\n",
+ "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+ "from sklearn.decomposition import PCA\n",
+ "from imblearn.over_sampling import SMOTE, RandomOverSampler\n",
+ "from imblearn.under_sampling import RandomUnderSampler"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6804c79",
+ "metadata": {},
+ "source": [
+ "https://archive.ics.uci.edu/dataset/222/bank+marketing\n",
+ "\n",
+ "https://www.kaggle.com/datasets/janiobachmann/bank-marketing-dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e94be900",
+ "metadata": {},
+ "source": [
+ "# Notes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c718ba8",
+ "metadata": {},
+ "source": [
+ "Heavy class imbalance in default and loan"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8ac2733",
+ "metadata": {},
+ "source": [
+ "# FUNCTIONS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3475dee6",
+ "metadata": {},
+ "source": [
+ "All functions used in this project are in this section"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "046d5abe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Read dataset, check for null and duplicates\n",
+ "def read_dataset(dataset):\n",
+ " \n",
+ " data = pd.read_csv(dataset)\n",
+ " nrow = len(data.index)\n",
+ " ncol = len(data.columns)\n",
+ "\n",
+ " print(\"The dataset contains\", format(nrow, \",d\"), \"rows and\", ncol, \"columns.\")\n",
+ " \n",
+ " #Check for null values\n",
+ " if ((data.isna().sum()).sum()) > 0:\n",
+ " print(\"There are null items in the dataset\")\n",
+ " else:\n",
+ " print(\"There are no null items in the dataset\")\n",
+ " \n",
+ " #Check for duplicates\n",
+ " \n",
+ " #col_names = [\"Country\", \"Year\", \"Status\"]\n",
+ " #(data.duplicated(subset=col_names)).sum()\n",
+ "\n",
+ " if (data.duplicated().sum()) > 0:\n",
+ " print(\"There are duplicates in the dataset\")\n",
+ " else:\n",
+ " print(\"There are no duplicates in the dataset\")\n",
+ " \n",
+ " return data\n",
+ "\n",
+ "\n",
+ "\n",
+ "#Function to categorize data into numeric and categorical\n",
+ "def categorize_data(data):\n",
+ " \n",
+ " numeric=[]\n",
+ " categorical=[]\n",
+ " numeric_dtypes = [\"int64\", \"int32\", \"float64\", \"float32\"]\n",
+ "\n",
+ " for i in range (len(data.columns)):\n",
+ " if data[data.columns[i]].dtype in numeric_dtypes:\n",
+ " numeric.append(data.columns[i])\n",
+ " else:\n",
+ " categorical.append(data.columns[i])\n",
+ " \n",
+ " return numeric, categorical\n",
+ "\n",
+ " \n",
+ "#Function to check for outliers\n",
+ "def outliers_check(data, numeric):\n",
+ " outliers_sum =[]\n",
+ "\n",
+ " for i in range(len(numeric_cols)):\n",
+ " Q1 = data[numeric_cols[i]].quantile(0.25)\n",
+ " Q3 = data[numeric_cols[i]].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ " outliers = (data[numeric_cols[i]] < (Q1 - 1.5 * IQR)) | (data[numeric_cols[i]] > (Q3 + 1.5 * IQR))\n",
+ " print(numeric_cols[i], \"\",outliers.sum())\n",
+ " outliers_sum.append(outliers.sum())\n",
+ "\n",
+ " return outliers.sum()\n",
+ "\n",
+ "\n",
+ "def remove_duplicates(data):\n",
+ " duplicated_sum = data.duplicated().sum()\n",
+ " if duplicated_sum == 0:\n",
+ " print(\"Number of duplicated rows in dataset =\", duplicated_sum)\n",
+ " return data\n",
+ " else:\n",
+ " print(\"Number of duplicated rows in dataset =\", duplicated_sum)\n",
+ " data = data[~data.duplicated()]\n",
+ " print(\"Duplicated rows have been removed\")\n",
+ " return data\n",
+ "\n",
+ " \n",
+ " \n",
+ "def oneHotEncoding(data, categorical):\n",
+ "\n",
+ " encoder = OneHotEncoder(sparse=False, drop='first') # 'drop' parameter removes one of the dummy variables to avoid multicollinearity\n",
+ "\n",
+ " encoded_data = encoder.fit_transform(data[categorical_cols])\n",
+ "\n",
+ " data_encoded = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out(categorical_cols))\n",
+ "\n",
+ " data_final = pd.concat([data.drop(columns=categorical_cols), data_encoded], axis=1)\n",
+ "\n",
+ " return data_final"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64d45954",
+ "metadata": {},
+ "source": [
+ "# EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "c6fafe71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The dataset contains 11,162 rows and 17 columns.\n",
+ "There are no null items in the dataset\n",
+ "There are no duplicates in the dataset\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = read_dataset(\"bank.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c54135de",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>age</th>\n",
+ " <th>job</th>\n",
+ " <th>marital</th>\n",
+ " <th>education</th>\n",
+ " <th>default</th>\n",
+ " <th>balance</th>\n",
+ " <th>housing</th>\n",
+ " <th>loan</th>\n",
+ " <th>contact</th>\n",
+ " <th>day</th>\n",
+ " <th>month</th>\n",
+ " <th>duration</th>\n",
+ " <th>campaign</th>\n",
+ " <th>pdays</th>\n",
+ " <th>previous</th>\n",
+ " <th>poutcome</th>\n",
+ " <th>deposit</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>59</td>\n",
+ " <td>admin.</td>\n",
+ " <td>married</td>\n",
+ " <td>secondary</td>\n",
+ " <td>no</td>\n",
+ " <td>2343</td>\n",
+ " <td>yes</td>\n",
+ " <td>no</td>\n",
+ " <td>unknown</td>\n",
+ " <td>5</td>\n",
+ " <td>may</td>\n",
+ " <td>1042</td>\n",
+ " <td>1</td>\n",
+ " <td>-1</td>\n",
+ " <td>0</td>\n",
+ " <td>unknown</td>\n",
+ " <td>yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>56</td>\n",
+ " <td>admin.</td>\n",
+ " <td>married</td>\n",
+ " <td>secondary</td>\n",
+ " <td>no</td>\n",
+ " <td>45</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>unknown</td>\n",
+ " <td>5</td>\n",
+ " <td>may</td>\n",
+ " <td>1467</td>\n",
+ " <td>1</td>\n",
+ " <td>-1</td>\n",
+ " <td>0</td>\n",
+ " <td>unknown</td>\n",
+ " <td>yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>41</td>\n",
+ " <td>technician</td>\n",
+ " <td>married</td>\n",
+ " <td>secondary</td>\n",
+ " <td>no</td>\n",
+ " <td>1270</td>\n",
+ " <td>yes</td>\n",
+ " <td>no</td>\n",
+ " <td>unknown</td>\n",
+ " <td>5</td>\n",
+ " <td>may</td>\n",
+ " <td>1389</td>\n",
+ " <td>1</td>\n",
+ " <td>-1</td>\n",
+ " <td>0</td>\n",
+ " <td>unknown</td>\n",
+ " <td>yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>55</td>\n",
+ " <td>services</td>\n",
+ " <td>married</td>\n",
+ " <td>secondary</td>\n",
+ " <td>no</td>\n",
+ " <td>2476</td>\n",
+ " <td>yes</td>\n",
+ " <td>no</td>\n",
+ " <td>unknown</td>\n",
+ " <td>5</td>\n",
+ " <td>may</td>\n",
+ " <td>579</td>\n",
+ " <td>1</td>\n",
+ " <td>-1</td>\n",
+ " <td>0</td>\n",
+ " <td>unknown</td>\n",
+ " <td>yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>54</td>\n",
+ " <td>admin.</td>\n",
+ " <td>married</td>\n",
+ " <td>tertiary</td>\n",
+ " <td>no</td>\n",
+ " <td>184</td>\n",
+ " <td>no</td>\n",
+ " <td>no</td>\n",
+ " <td>unknown</td>\n",
+ " <td>5</td>\n",
+ " <td>may</td>\n",
+ " <td>673</td>\n",
+ " <td>2</td>\n",
+ " <td>-1</td>\n",
+ " <td>0</td>\n",
+ " <td>unknown</td>\n",
+ " <td>yes</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age job marital education default balance housing loan contact \\\n",
+ "0 59 admin. married secondary no 2343 yes no unknown \n",
+ "1 56 admin. married secondary no 45 no no unknown \n",
+ "2 41 technician married secondary no 1270 yes no unknown \n",
+ "3 55 services married secondary no 2476 yes no unknown \n",
+ "4 54 admin. married tertiary no 184 no no unknown \n",
+ "\n",
+ " day month duration campaign pdays previous poutcome deposit \n",
+ "0 5 may 1042 1 -1 0 unknown yes \n",
+ "1 5 may 1467 1 -1 0 unknown yes \n",
+ "2 5 may 1389 1 -1 0 unknown yes \n",
+ "3 5 may 579 1 -1 0 unknown yes \n",
+ "4 5 may 673 2 -1 0 unknown yes "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.set_option('display.max_columns', None)\n",
+ "\n",
+ "(data).head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f6148ae5",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 11162 entries, 0 to 11161\n",
+ "Data columns (total 17 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 age 11162 non-null int64 \n",
+ " 1 job 11162 non-null object\n",
+ " 2 marital 11162 non-null object\n",
+ " 3 education 11162 non-null object\n",
+ " 4 default 11162 non-null object\n",
+ " 5 balance 11162 non-null int64 \n",
+ " 6 housing 11162 non-null object\n",
+ " 7 loan 11162 non-null object\n",
+ " 8 contact 11162 non-null object\n",
+ " 9 day 11162 non-null int64 \n",
+ " 10 month 11162 non-null object\n",
+ " 11 duration 11162 non-null int64 \n",
+ " 12 campaign 11162 non-null int64 \n",
+ " 13 pdays 11162 non-null int64 \n",
+ " 14 previous 11162 non-null int64 \n",
+ " 15 poutcome 11162 non-null object\n",
+ " 16 deposit 11162 non-null object\n",
+ "dtypes: int64(7), object(10)\n",
+ "memory usage: 1.4+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d3639f64",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>age</th>\n",
+ " <th>balance</th>\n",
+ " <th>day</th>\n",
+ " <th>duration</th>\n",
+ " <th>campaign</th>\n",
+ " <th>pdays</th>\n",
+ " <th>previous</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>11162.000000</td>\n",
+ " <td>11162.000000</td>\n",
+ " <td>11162.000000</td>\n",
+ " <td>11162.000000</td>\n",
+ " <td>11162.000000</td>\n",
+ " <td>11162.000000</td>\n",
+ " <td>11162.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>41.231948</td>\n",
+ " <td>1528.538524</td>\n",
+ " <td>15.658036</td>\n",
+ " <td>371.993818</td>\n",
+ " <td>2.508421</td>\n",
+ " <td>51.330407</td>\n",
+ " <td>0.832557</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>11.913369</td>\n",
+ " <td>3225.413326</td>\n",
+ " <td>8.420740</td>\n",
+ " <td>347.128386</td>\n",
+ " <td>2.722077</td>\n",
+ " <td>108.758282</td>\n",
+ " <td>2.292007</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>18.000000</td>\n",
+ " <td>-6847.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>32.000000</td>\n",
+ " <td>122.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>138.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>39.000000</td>\n",
+ " <td>550.000000</td>\n",
+ " <td>15.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>-1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>49.000000</td>\n",
+ " <td>1708.000000</td>\n",
+ " <td>22.000000</td>\n",
+ " <td>496.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>20.750000</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>95.000000</td>\n",
+ " <td>81204.000000</td>\n",
+ " <td>31.000000</td>\n",
+ " <td>3881.000000</td>\n",
+ " <td>63.000000</td>\n",
+ " <td>854.000000</td>\n",
+ " <td>58.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age balance day duration campaign \\\n",
+ "count 11162.000000 11162.000000 11162.000000 11162.000000 11162.000000 \n",
+ "mean 41.231948 1528.538524 15.658036 371.993818 2.508421 \n",
+ "std 11.913369 3225.413326 8.420740 347.128386 2.722077 \n",
+ "min 18.000000 -6847.000000 1.000000 2.000000 1.000000 \n",
+ "25% 32.000000 122.000000 8.000000 138.000000 1.000000 \n",
+ "50% 39.000000 550.000000 15.000000 255.000000 2.000000 \n",
+ "75% 49.000000 1708.000000 22.000000 496.000000 3.000000 \n",
+ "max 95.000000 81204.000000 31.000000 3881.000000 63.000000 \n",
+ "\n",
+ " pdays previous \n",
+ "count 11162.000000 11162.000000 \n",
+ "mean 51.330407 0.832557 \n",
+ "std 108.758282 2.292007 \n",
+ "min -1.000000 0.000000 \n",
+ "25% -1.000000 0.000000 \n",
+ "50% -1.000000 0.000000 \n",
+ "75% 20.750000 1.000000 \n",
+ "max 854.000000 58.000000 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Summary statistics of the dataset\n",
+ "data.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "e33d1922",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "no 5873\n",
+ "yes 5289\n",
+ "Name: deposit, dtype: int64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Class imbalance\n",
+ "data['deposit'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "277920d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Categorize data into numeric and categorical\n",
+ "numeric_cols, categorical_cols = categorize_data(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b9c4d65",
+ "metadata": {},
+ "source": [
+ "# Visualisations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "77615c11",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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DQUFBCAoKQkZGBjw8PJCUlAQAkCQJ8+bNw/Lly+Hr6wsfHx+sWLECEydOlFcVh4SEYPr06UhOTsamTZsAAPPnz0d8fDxXEBMRkWKcHrD79u3DmTNn8NRTT3U6tnLlSrS0tGDhwoUwm82IiIjA3r174eXlJdds2LABarUaiYmJaGlpQUxMDLZv3w4XFxe5Ji8vD0uWLJFXGyckJCAnJ0f5zhER0aClEkIIZzfiZmC1WiFJEiwWS4+fx159Nvzwax9jqLfPDeu/sV7ER8/Fo66ursuV0ERE1Le6kwX94hksERHRQMOAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTBgiYiIFMCAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTg9YM+dO4c5c+bA19cXHh4emDRpEkpLS+XjQgikp6dDr9fD3d0d0dHRqKiosLuGzWZDamoq/Pz84OnpiYSEBJw9e9auxmw2w2AwQJIkSJIEg8GAxsbGvugiERENQk4NWLPZjHvvvReurq7461//ipMnT+KNN97A8OHD5Zr169cjKysLOTk5KCkpgU6nQ2xsLJqamuSatLQ07N69G/n5+Th06BCam5sRHx+P9vZ2uSYpKQllZWUwGo0wGo0oKyuDwWDoy+4SEdEgonbmm69btw4BAQHYtm2bvG/cuHHyn4UQyM7OxurVqzF79mwAwI4dO6DVarFr1y6kpKTAYrFg69at2LlzJ6ZOnQoAyM3NRUBAAPbt24dp06ahsrISRqMRRUVFiIiIAABs2bIFkZGRqKqqQnBwcN91moiIBgWnzmD37NmD8PBw/OIXv4C/vz/uvPNObNmyRT5eXV0Nk8mEuLg4eZ9Go0FUVBQOHz4MACgtLUVbW5tdjV6vR2hoqFxz5MgRSJIkhysATJ48GZIkyTXXstlssFqtdhsREZGjnBqw//rXv7Bx40YEBQXh008/xYIFC7BkyRK89957AACTyQQA0Gq1dudptVr5mMlkgpubG0aMGHHdGn9//07v7+/vL9dcKzMzU35eK0kSAgICflhniYhoUHFqwHZ0dOCuu+5CRkYG7rzzTqSkpCA5ORkbN260q1OpVHavhRCd9l3r2pqu6q93nVWrVsFischbTU2No90iIiJybsCOGjUKEyZMsNsXEhKCM2fOAAB0Oh0AdJpl1tfXy7NanU6H1tZWmM3m69bU1dV1ev+GhoZOs+OrNBoNvL297TYiIiJHOTVg7733XlRVVdnt++qrrzB27FgAQGBgIHQ6HQoKCuTjra2tKCwsxJQpUwAAYWFhcHV1taupra1FeXm5XBMZGQmLxYLi4mK55ujRo7BYLHINERFRb3LqKuJnn30WU6ZMQUZGBhITE1FcXIzNmzdj8+bNAL69rZuWloaMjAwEBQUhKCgIGRkZ8PDwQFJSEgBAkiTMmzcPy5cvh6+vL3x8fLBixQpMnDhRXlUcEhKC6dOnIzk5GZs2bQIAzJ8/H/Hx8VxBTEREinBqwN59993YvXs3Vq1ahbVr1yIwMBDZ2dl4/PHH5ZqVK1eipaUFCxcuhNlsRkREBPbu3QsvLy+5ZsOGDVCr1UhMTERLSwtiYmKwfft2uLi4yDV5eXlYsmSJvNo4ISEBOTk5fddZIiIaVFRCCOHsRtwMrFYrJEmCxWLp8fPYq8+FH37tYwz19rlh/TfWi/jouXjU1dV1uQqaiIj6VneywOlflUhERDQQMWCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTBgiYiIFMCAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTBgiYiIFMCAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBTg3Y9PR0qFQqu02n08nHhRBIT0+HXq+Hu7s7oqOjUVFRYXcNm82G1NRU+Pn5wdPTEwkJCTh79qxdjdlshsFggCRJkCQJBoMBjY2NfdFFIiIapJw+g73ttttQW1srbydOnJCPrV+/HllZWcjJyUFJSQl0Oh1iY2PR1NQk16SlpWH37t3Iz8/HoUOH0NzcjPj4eLS3t8s1SUlJKCsrg9FohNFoRFlZGQwGQ5/2k4iIBhe10xugVtvNWq8SQiA7OxurV6/G7NmzAQA7duyAVqvFrl27kJKSAovFgq1bt2Lnzp2YOnUqACA3NxcBAQHYt28fpk2bhsrKShiNRhQVFSEiIgIAsGXLFkRGRqKqqgrBwcF911kiIho0nD6D/frrr6HX6xEYGIhHH30U//rXvwAA1dXVMJlMiIuLk2s1Gg2ioqJw+PBhAEBpaSna2trsavR6PUJDQ+WaI0eOQJIkOVwBYPLkyZAkSa7pis1mg9VqtduIiIgc5dSAjYiIwHvvvYdPP/0UW7ZsgclkwpQpU3DhwgWYTCYAgFartTtHq9XKx0wmE9zc3DBixIjr1vj7+3d6b39/f7mmK5mZmfIzW0mSEBAQ8IP6SkREg4tTA3bGjBn4z//8T0ycOBFTp07F//zP/wD49lbwVSqVyu4cIUSnfde6tqar+htdZ9WqVbBYLPJWU1PjUJ+IiIiAfnCL+Ls8PT0xceJEfP311/Jz2WtnmfX19fKsVqfTobW1FWaz+bo1dXV1nd6roaGh0+z4uzQaDby9ve02IiIiR/UoYMePH48LFy502t/Y2Ijx48f3uDE2mw2VlZUYNWoUAgMDodPpUFBQIB9vbW1FYWEhpkyZAgAICwuDq6urXU1tbS3Ky8vlmsjISFgsFhQXF8s1R48ehcVikWuIiIh6W49WEZ86dcruYzBX2Ww2nDt3zuHrrFixArNmzcKYMWNQX1+Pl19+GVarFXPnzoVKpUJaWhoyMjIQFBSEoKAgZGRkwMPDA0lJSQAASZIwb948LF++HL6+vvDx8cGKFSvkW84AEBISgunTpyM5ORmbNm0CAMyfPx/x8fFcQUxERIrpVsDu2bNH/vOnn34KSZLk1+3t7di/fz/GjRvn8PXOnj2Lxx57DP/+978xcuRITJ48GUVFRRg7diwAYOXKlWhpacHChQthNpsRERGBvXv3wsvLS77Ghg0boFarkZiYiJaWFsTExGD79u1wcXGRa/Ly8rBkyRJ5tXFCQgJycnK603UiIqJuUQkhhKPFQ4Z8e0dZpVLh2tNcXV0xbtw4vPHGG4iPj+/dVvYDVqsVkiTBYrH0+Hns1WfDD7/2MYZ6+9yw/hvrRXz0XDzq6uq6XAlNRER9qztZ0K0ZbEdHBwAgMDAQJSUl8PPz63kriYiIBrAePYOtrq7u7XYQERENKD3+qsT9+/dj//79qK+vl2e2V/33f//3D24YERHRzaxHAfvSSy9h7dq1CA8Px6hRo274xQ9ERESDTY8C9t1338X27dv5izRERETfo0dfNNHa2sovaSAiIrqOHgXs008/jV27dvV2W4iIiAaMHt0i/uabb7B582bs27cPt99+O1xdXe2OZ2Vl9UrjiIiIblY9CtgvvvgCkyZNAgCUl5fbHeOCJyIioh4G7MGDB3u7HURERANKv/q5OiIiooGiRzPYBx544Lq3gg8cONDjBhEREQ0EPQrYq89fr2pra0NZWRnKy8sxd+7c3mgXERHRTa1HAbthw4Yu96enp6O5ufkHNYiIiGgg6NVnsHPmzOH3EBMREaGXA/bIkSMYOnRob16SiIjoptSjW8SzZ8+2ey2EQG1tLY4dO4Zf//rXvdIwIiKim1mPAlaSJLvXQ4YMQXBwMNauXYu4uLheaRgREdHNrEcBu23btt5uBxER0YDS4x9cB4DS0lJUVlZCpVJhwoQJuPPOO3urXURERDe1HgVsfX09Hn30UXz22WcYPnw4hBCwWCx44IEHkJ+fj5EjR/Z2O4mIiG4qPVpFnJqaCqvVioqKCly8eBFmsxnl5eWwWq1YsmRJb7eRiIjoptOjGazRaMS+ffsQEhIi75swYQLefvttLnIiIiJCD2ewHR0dnX4DFgBcXV3R0dHxgxtFRER0s+tRwD744INYunQpzp8/L+87d+4cnn32WcTExPSoIZmZmVCpVEhLS5P3CSGQnp4OvV4Pd3d3REdHo6Kiwu48m82G1NRU+Pn5wdPTEwkJCTh79qxdjdlshsFggCRJkCQJBoMBjY2NPWonERGRI3oUsDk5OWhqasK4cePwox/9CD/+8Y8RGBiIpqYmvPXWW92+XklJCTZv3ozbb7/dbv/69euRlZWFnJwclJSUQKfTITY2Fk1NTXJNWloadu/ejfz8fBw6dAjNzc2Ij49He3u7XJOUlISysjIYjUYYjUaUlZXBYDD0pOtEREQO6dEz2ICAABw/fhwFBQX48ssvIYTAhAkTMHXq1G5fq7m5GY8//ji2bNmCl19+Wd4vhEB2djZWr14tf3PUjh07oNVqsWvXLqSkpMBisWDr1q3YuXOn/N65ubkICAjAvn37MG3aNFRWVsJoNKKoqAgREREAgC1btiAyMhJVVVUIDg7uyRAQERFdV7dmsAcOHMCECRNgtVoBALGxsUhNTcWSJUtw991347bbbsPf/va3bjVg0aJFmDlzZqdwrq6uhslksls0pdFoEBUVhcOHDwP49nO4bW1tdjV6vR6hoaFyzZEjRyBJkhyuADB58mRIkiTXdMVms8FqtdptREREjupWwGZnZyM5ORne3t6djkmShJSUFGRlZTl8vfz8fBw/fhyZmZmdjplMJgCAVqu126/VauVjJpMJbm5uGDFixHVr/P39O13f399frulKZmam/MxWkiQEBAQ43C8iIqJuBeznn3+O6dOnf+/xuLg4lJaWOnStmpoaLF26FLm5udf9BR6VSmX3WgjRad+1rq3pqv5G11m1ahUsFou81dTUXPc9iYiIvqtbAVtXV9flx3OuUqvVaGhocOhapaWlqK+vR1hYGNRqNdRqNQoLC/Hmm29CrVbLM9drZ5n19fXyMZ1Oh9bWVpjN5uvW1NXVdXr/hoaGTrPj79JoNPD29rbbiIiIHNWtgL3llltw4sSJ7z3+xRdfYNSoUQ5dKyYmBidOnEBZWZm8hYeH4/HHH0dZWRnGjx8PnU6HgoIC+ZzW1lYUFhZiypQpAICwsDC4urra1dTW1qK8vFyuiYyMhMViQXFxsVxz9OhRWCwWuYaIiKi3dWsV8UMPPYTf/OY3mDFjRqfbui0tLVizZg3i4+MdupaXlxdCQ0Pt9nl6esLX11fen5aWhoyMDAQFBSEoKAgZGRnw8PBAUlISgG+f+86bNw/Lly+Hr68vfHx8sGLFCkycOFFeNBUSEoLp06cjOTkZmzZtAgDMnz8f8fHxXEFMRESK6VbAvvjii/jwww/xk5/8BIsXL0ZwcDBUKhUqKyvx9ttvo729HatXr+61xq1cuRItLS1YuHAhzGYzIiIisHfvXnh5eck1GzZsgFqtRmJiIlpaWhATE4Pt27fDxcVFrsnLy8OSJUvk1cYJCQnIycnptXYSERFdSyWEEN054fTp03jmmWfw6aef4uqpKpUK06ZNwzvvvINx48Yp0U6ns1qtkCQJFoulx89jrz4bfvi1jzHU2+eG9d9YL+Kj5+JRV1fX5UpoIiLqW93Jgm5/0cTYsWPxySefwGw245///CeEEAgKCur0URkiIqLBrMc/uD5ixAjcfffdvdkWIiKiAaNH30VMRERE18eAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTBgiYiIFMCAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSgFMDduPGjbj99tvh7e0Nb29vREZG4q9//at8XAiB9PR06PV6uLu7Izo6GhUVFXbXsNlsSE1NhZ+fHzw9PZGQkICzZ8/a1ZjNZhgMBkiSBEmSYDAY0NjY2BddJCKiQcqpATt69Gi8+uqrOHbsGI4dO4YHH3wQDz/8sByi69evR1ZWFnJyclBSUgKdTofY2Fg0NTXJ10hLS8Pu3buRn5+PQ4cOobm5GfHx8Whvb5drkpKSUFZWBqPRCKPRiLKyMhgMhj7vLxERDR4qIYRwdiO+y8fHB6+99hqeeuop6PV6pKWl4fnnnwfw7WxVq9Vi3bp1SElJgcViwciRI7Fz50488sgjAIDz588jICAAn3zyCaZNm4bKykpMmDABRUVFiIiIAAAUFRUhMjISX375JYKDgx1ql9VqhSRJsFgs8Pb27lHf6uvrodVq8fBrH2Oot88N67+xXsRHz8Wjrq4O/v7+PXpPIiLqPd3Jgn7zDLa9vR35+fm4dOkSIiMjUV1dDZPJhLi4OLlGo9EgKioKhw8fBgCUlpaira3Nrkav1yM0NFSuOXLkCCRJksMVACZPngxJkuSarthsNlitVruNiIjIUU4P2BMnTmDYsGHQaDRYsGABdu/ejQkTJsBkMgEAtFqtXb1Wq5WPmUwmuLm5YcSIEdet6Wr25+/vL9d0JTMzU35mK0kSAgICflA/iYhocHF6wAYHB6OsrAxFRUV45plnMHfuXJw8eVI+rlKp7OqFEJ32Xevamq7qb3SdVatWwWKxyFtNTY2jXSIiInJ+wLq5ueHHP/4xwsPDkZmZiTvuuAO/+93voNPpAKDTLPPqc0wA0Ol0aG1thdlsvm5NXV1dp/dtaGjoNDv+Lo1GI69uvroRERE5yukBey0hBGw2GwIDA6HT6VBQUCAfa21tRWFhIaZMmQIACAsLg6urq11NbW0tysvL5ZrIyEhYLBYUFxfLNUePHoXFYpFriIiIepvamW/+q1/9CjNmzEBAQACampqQn5+Pzz77DEajESqVCmlpacjIyEBQUBCCgoKQkZEBDw8PJCUlAQAkScK8efOwfPly+Pr6wsfHBytWrMDEiRMxdepUAEBISAimT5+O5ORkbNq0CQAwf/58xMfHO7yCmIiIqLucGrB1dXUwGAyora2FJEm4/fbbYTQaERsbCwBYuXIlWlpasHDhQpjNZkRERGDv3r3w8vKSr7Fhwwao1WokJiaipaUFMTEx2L59O1xcXOSavLw8LFmyRF5tnJCQgJycnL7tLBERDSr97nOw/RU/B0tERDfl52CJiIgGEgYsERGRAhiwRERECmDAEhERKYABS0REpAAGLBERkQIYsERERApgwBIRESmAAUtERKQABiwREZECGLBEREQKYMASEREpgAFLRESkAAYsERGRAhiwRERECmDAEhERKYABS0REpAAGLBERkQIYsERERApgwBIRESmAAUtERKQABiwREZECGLBEREQKYMASEREpwKkBm5mZibvvvhteXl7w9/fHT3/6U1RVVdnVCCGQnp4OvV4Pd3d3REdHo6Kiwq7GZrMhNTUVfn5+8PT0REJCAs6ePWtXYzabYTAYIEkSJEmCwWBAY2Oj0l0kIqJByqkBW1hYiEWLFqGoqAgFBQW4cuUK4uLicOnSJblm/fr1yMrKQk5ODkpKSqDT6RAbG4umpia5Ji0tDbt370Z+fj4OHTqE5uZmxMfHo729Xa5JSkpCWVkZjEYjjEYjysrKYDAY+rS/REQ0eKiEEMLZjbiqoaEB/v7+KCwsxP333w8hBPR6PdLS0vD8888D+Ha2qtVqsW7dOqSkpMBisWDkyJHYuXMnHnnkEQDA+fPnERAQgE8++QTTpk1DZWUlJkyYgKKiIkRERAAAioqKEBkZiS+//BLBwcE3bJvVaoUkSbBYLPD29u5R/+rr66HVavHwax9jqLfPDeu/sV7ER8/Fo66uDv7+/j16TyIi6j3dyYJ+9QzWYrEAAHx8vg2f6upqmEwmxMXFyTUajQZRUVE4fPgwAKC0tBRtbW12NXq9HqGhoXLNkSNHIEmSHK4AMHnyZEiSJNdcy2azwWq12m1ERESO6jcBK4TAsmXLcN999yE0NBQAYDKZAABardauVqvVysdMJhPc3NwwYsSI69Z0NQP09/eXa66VmZkpP6+VJAkBAQE/rINERDSo9JuAXbx4Mb744gv8/ve/73RMpVLZvRZCdNp3rWtruqq/3nVWrVoFi8UibzU1NY50g4iICEA/CdjU1FTs2bMHBw8exOjRo+X9Op0OADrNMq8+y7xa09raCrPZfN2aurq6Tu/b0NDQaXZ8lUajgbe3t91GRETkKKcGrBACixcvxocffogDBw4gMDDQ7nhgYCB0Oh0KCgrkfa2trSgsLMSUKVMAAGFhYXB1dbWrqa2tRXl5uVwTGRkJi8WC4uJiuebo0aOwWCxyDRERUW9SO/PNFy1ahF27duGjjz6Cl5eXPFOVJAnu7u5QqVRIS0tDRkYGgoKCEBQUhIyMDHh4eCApKUmunTdvHpYvXw5fX1/4+PhgxYoVmDhxIqZOnQoACAkJwfTp05GcnIxNmzYBAObPn4/4+HiHVhATERF1l1MDduPGjQCA6Ohou/3btm3Dk08+CQBYuXIlWlpasHDhQpjNZkRERGDv3r3w8vKS6zds2AC1Wo3ExES0tLQgJiYG27dvh4uLi1yTl5eHJUuWyKuNExISkJOTo2wHiYho0OpXn4Ptz/g5WCIiumk/B0tERDRQMGCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTBgiYiIFMCAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSAAOWiIhIAQxYIiIiBTBgiYiIFMCAJSIiUgADloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBTg3Y//3f/8WsWbOg1+uhUqnw5z//2e64EALp6enQ6/Vwd3dHdHQ0Kioq7GpsNhtSU1Ph5+cHT09PJCQk4OzZs3Y1ZrMZBoMBkiRBkiQYDAY0NjYq3DsiIhrMnBqwly5dwh133IGcnJwuj69fvx5ZWVnIyclBSUkJdDodYmNj0dTUJNekpaVh9+7dyM/Px6FDh9Dc3Iz4+Hi0t7fLNUlJSSgrK4PRaITRaERZWRkMBoPi/SMiosFL7cw3nzFjBmbMmNHlMSEEsrOzsXr1asyePRsAsGPHDmi1WuzatQspKSmwWCzYunUrdu7cialTpwIAcnNzERAQgH379mHatGmorKyE0WhEUVERIiIiAABbtmxBZGQkqqqqEBwc3DedJSKiQaXfPoOtrq6GyWRCXFycvE+j0SAqKgqHDx8GAJSWlqKtrc2uRq/XIzQ0VK45cuQIJEmSwxUAJk+eDEmS5Jqu2Gw2WK1Wu42IiMhR/TZgTSYTAECr1drt12q18jGTyQQ3NzeMGDHiujX+/v6dru/v7y/XdCUzM1N+ZitJEgICAn5Qf4iIaHDptwF7lUqlsnsthOi071rX1nRVf6PrrFq1ChaLRd5qamq62XIiIhrM+m3A6nQ6AOg0y6yvr5dntTqdDq2trTCbzdetqaur63T9hoaGTrPj79JoNPD29rbbiIiIHNVvAzYwMBA6nQ4FBQXyvtbWVhQWFmLKlCkAgLCwMLi6utrV1NbWory8XK6JjIyExWJBcXGxXHP06FFYLBa5hoiIqLc5dRVxc3Mz/vnPf8qvq6urUVZWBh8fH4wZMwZpaWnIyMhAUFAQgoKCkJGRAQ8PDyQlJQEAJEnCvHnzsHz5cvj6+sLHxwcrVqzAxIkT5VXFISEhmD59OpKTk7Fp0yYAwPz58xEfH88VxEREpBinBuyxY8fwwAMPyK+XLVsGAJg7dy62b9+OlStXoqWlBQsXLoTZbEZERAT27t0LLy8v+ZwNGzZArVYjMTERLS0tiImJwfbt2+Hi4iLX5OXlYcmSJfJq44SEhO/97C0REVFvUAkhhLMbcTOwWq2QJAkWi6XHz2OvPht++LWPMdTb54b131gv4qPn4lFXV9flSmgiIupb3cmCfvsMloiI6GbGgCUiIlIAA5aIiEgBTl3kRPRdzc3NuHz5ssP1Hh4eGDZsmIItIiLqOQbsINedUFMy0JqbmzFm7DiYL15w+JwRPr44c/oUQ5aI+iUG7CDW3VBTMtAuX74M88ULmL4mF5phw29Yb2tuhPGlObh8+TIDloj6JQbsINadUOurQNMMG+7QR5iIiPo7Biwx1IiIFMBVxERERApgwBIRESmAAUtERKQABiwREZECGLBEREQKYMASEREpgAFLRESkAAYsERGRAhiwRERECmDAEhERKYBflUjUz/Fn/IhuTgxYon6MP+NHdPNiwBL1Y/wZP6KbFwOWqAv97bYsf/GI6ObDgKVuaWhocLi2o6MDQ4Y4to6uO9dVWl/clnU0wPvTuBBR9zBgB5juzLy684/3FVsLoBqC0NBQh89RDVFDdFxxuB4A2js6ulWvhJ7elj19+jRGjhx5w/pLly7hrrBwNJovOtym/jAuRNQ9gypg33nnHbz22muora3FbbfdhuzsbPzHf/yHs5vVa3oy8wIc+8e7vc0GiA5M/dV2eI7wu2G9te40Dr6+qNv1He3dCxJH/5PQk9m0o7dle/KfDwCIffE9eEjXv77S4wJw1TGRUgZNwL7//vtIS0vDO++8g3vvvRebNm3CjBkzcPLkSYwZM8bZzbsuR/+xbGho6NbMqyf/eLs5GDq25sYe1Tuqu6Gm5Gy6p//5cPXwvuHYKD0uAFcdEyll0ARsVlYW5s2bh6effhoAkJ2djU8//RQbN25EZmamk1vXtZ7OjNQO/MMNdP8f7/6kO6HWV7Nppf4z0R3dDfvu3t4Gunc3oDu1Std399qc2feO/rZgsC8NioBtbW1FaWkpXnjhBbv9cXFxOHz4cJfn2Gw22Gw2+bXFYgEAWK3WHrejqakJAHDpwnm0fXPjv3BN9WcA0YHIlHXwGD7ihvXN/z6Po1vT0Vx/Dh2t39yw/tJFEwDg8oVaoL2112r7sv6KreWGY3nF1uJw7Xfr+0NflRwXAPimyQxA1b3/xKlcANHe+7VK13fz2tLwEfjfws/g6enpeHvIzqVLl3B/VDQsjWaHz1F63D08PH7Qta9mgBDixsViEDh37pwAIP7+97/b7X/llVfET37yky7PWbNmjQDAjRs3bty4ddpqampumD2DYgZ7lUqlsnsthOi076pVq1Zh2bJl8uuOjg5cvHgRvr6+33uOI6xWKwICAlBTUwNvb+8eX2ew4zj2Ho5l7+A49o7+Po5CCDQ1NUGv19+wdlAErJ+fH1xcXGAymez219fXQ6vVdnmORqOBRqOx2zd8+PBea5O3t3e//Mtzs+E49h6OZe/gOPaO/jyOkiQ5VDcofk3Hzc0NYWFhKCgosNtfUFCAKVOmOKlVREQ0kA2KGSwALFu2DAaDAeHh4YiMjMTmzZtx5swZLFiwwNlNIyKiAWjQBOwjjzyCCxcuYO3ataitrUVoaCg++eQTjB07tk/bodFosGbNmk63n6l7OI69h2PZOziOvWMgjaNKCEfWGhMREVF3DIpnsERERH2NAUtERKQABiwREZECGLBEREQKYMD2oXfeeQeBgYEYOnQowsLC8Le//c3ZTepXMjMzcffdd8PLywv+/v746U9/iqqqKrsaIQTS09Oh1+vh7u6O6OhoVFRU2NXYbDakpqbCz88Pnp6eSEhIwNmzZ/uyK/1KZmYmVCoV0tLS5H0cR8edO3cOc+bMga+vLzw8PDBp0iSUlpbKxzmWN3blyhW8+OKLCAwMhLu7O8aPH4+1a9ei4zu/WDUgx/EHfckvOSw/P1+4urqKLVu2iJMnT4qlS5cKT09Pcfr0aWc3rd+YNm2a2LZtmygvLxdlZWVi5syZYsyYMaK5uVmuefXVV4WXl5f44IMPxIkTJ8QjjzwiRo0aJaxWq1yzYMECccstt4iCggJx/Phx8cADD4g77rhDXLlyxRndcqri4mIxbtw4cfvtt4ulS5fK+zmOjrl48aIYO3asePLJJ8XRo0dFdXW12Ldvn/jnP/8p13Asb+zll18Wvr6+4uOPPxbV1dXij3/8oxg2bJjIzs6WawbiODJg+8g999wjFixYYLfv1ltvFS+88IKTWtT/1dfXCwCisLBQCCFER0eH0Ol04tVXX5VrvvnmGyFJknj33XeFEEI0NjYKV1dXkZ+fL9ecO3dODBkyRBiNxr7tgJM1NTWJoKAgUVBQIKKiouSA5Tg67vnnnxf33Xff9x7nWDpm5syZ4qmnnrLbN3v2bDFnzhwhxMAdR94i7gNXfy4vLi7Obv/1fi6P/u8nAn18vv2N1erqaphMJrtx1Gg0iIqKksextLQUbW1tdjV6vR6hoaGDbqwXLVqEmTNnYurUqXb7OY6O27NnD8LDw/GLX/wC/v7+uPPOO7Flyxb5OMfSMffddx/279+Pr776CgDw+eef49ChQ3jooYcADNxxHDTf5ORM//73v9He3t7phwW0Wm2nHyCgbwkhsGzZMtx3333yb5VeHauuxvH06dNyjZubG0aMGNGpZjCNdX5+Po4fP46SkpJOxziOjvvXv/6FjRs3YtmyZfjVr36F4uJiLFmyBBqNBk888QTH0kHPP/88LBYLbr31Vri4uKC9vR2vvPIKHnvsMQAD9+8kA7YPdefn8ga7xYsX44svvsChQ4c6HevJOA6msa6pqcHSpUuxd+9eDB069HvrOI431tHRgfDwcGRkZAAA7rzzTlRUVGDjxo144okn5DqO5fW9//77yM3Nxa5du3DbbbehrKwMaWlp0Ov1mDt3rlw30MaRt4j7QE9+Lm8wS01NxZ49e3Dw4EGMHj1a3q/T6QDguuOo0+nQ2toKs9n8vTUDXWlpKerr6xEWFga1Wg21Wo3CwkK8+eabUKvV8jhwHG9s1KhRmDBhgt2+kJAQnDlzBgD/TjrqueeewwsvvIBHH30UEydOhMFgwLPPPovMzEwAA3ccGbB9gD+X5xghBBYvXowPP/wQBw4cQGBgoN3xwMBA6HQ6u3FsbW1FYWGhPI5hYWFwdXW1q6mtrUV5efmgGeuYmBicOHECZWVl8hYeHo7HH38cZWVlGD9+PMfRQffee2+nj4p99dVX8o+E8O+kYy5fvowhQ+zjxsXFRf6YzoAdRyctrhp0rn5MZ+vWreLkyZMiLS1NeHp6ilOnTjm7af3GM888IyRJEp999pmora2Vt8uXL8s1r776qpAkSXz44YfixIkT4rHHHutyKf/o0aPFvn37xPHjx8WDDz7Yr5fy94XvriIWguPoqOLiYqFWq8Urr7wivv76a5GXlyc8PDxEbm6uXMOxvLG5c+eKW265Rf6Yzocffij8/PzEypUr5ZqBOI4M2D709ttvi7Fjxwo3Nzdx1113yR8/oW8B6HLbtm2bXNPR0SHWrFkjdDqd0Gg04v777xcnTpywu05LS4tYvHix8PHxEe7u7iI+Pl6cOXOmj3vTv1wbsBxHx/3lL38RoaGhQqPRiFtvvVVs3rzZ7jjH8sasVqtYunSpGDNmjBg6dKgYP368WL16tbDZbHLNQBxH/lwdERGRAvgMloiISAEMWCIiIgUwYImIiBTAgCUiIlIAA5aIiEgBDFgiIiIFMGCJiIgUwIAlIiJSAAOWiDoZN24csrOznd0MopsaA5aIiEgBDFgiIiIFMGCJBrjo6GgsXrwYixcvxvDhw+Hr64sXX3wRV7+GvL6+HrNmzYK7uzsCAwORl5fX6RpZWVmYOHEiPD09ERAQgIULF6K5uRkAcOnSJXh7e+NPf/qT3Tl/+ctf4OnpiaamJrS2tmLx4sUYNWoUhg4dinHjxsm/BUo0UDFgiQaBHTt2QK1W4+jRo3jzzTexYcMG/Nd//RcA4Mknn8SpU6dw4MAB/OlPf8I777yD+vp6u/OHDBmCN998E+Xl5dixYwcOHDiAlStXAgA8PT3x6KOPYtu2bXbnbNu2DT//+c/h5eWFN998E3v27MEf/vAHVFVVITc3F+PGjeuTvhM5jZN/zYeIFBYVFSVCQkJER0eHvO/5558XISEhoqqqSgAQRUVF8rHKykoBQGzYsOF7r/mHP/xB+Pr6yq+PHj0qXFxcxLlz54QQQjQ0NAhXV1fx2WefCSGESE1NFQ8++KBdG4gGOs5giQaByZMnQ6VSya8jIyPx9ddfo7KyEmq1GuHh4fKxW2+9FcOHD7c7/+DBg4iNjcUtt9wCLy8vPPHEE7hw4QIuXboEALjnnntw22234b333gMA7Ny5E2PGjMH9998P4NtZcllZGYKDg7FkyRLs3btX4R4TOR8DlmgQu3LlCgDYhe+1Tp8+jYceegihoaH44IMPUFpairfffhsA0NbWJtc9/fTT8m3ibdu24Ze//KV83bvuugvV1dX47W9/i5aWFiQmJuLnP/+5Ut0i6hcYsESDQFFRUafXQUFBCA0NxZUrV3Ds2DH5WFVVFRobG+XXx44dw5UrV/DGG29g8uTJ+MlPfoLz5893eo85c+bgzJkzePPNN1FRUYG5c+faHff29sYjjzyCLVu24P3338cHH3yAixcv9m5HifoRtbMbQETKq6mpwbJly5CSkoLjx4/jrbfewhtvvIHg4GBMnz4dycnJ2Lx5M9RqNdLS0uDu7i6f+6Mf/QhXrlzBW2+9hVmzZuHvf/873n333U7vMWLECMyePRvPPfcc4uLiMHr0aPnYhg0bMGrUKEyaNAlDhgzBH//4R+h0uk63ookGEs5giQaBJ554Ai0tLbjnnnuwaNEipKamYv78+QC+vZ0bEBCAqKgozJ49G/Pnz4e/v7987qRJk5CVlYV169YhNDQUeXl53/sRm3nz5qG1tRVPPfWU3f5hw4Zh3bp1CA8Px913341Tp07hk08+wZAh/CeIBi6VEP//w3BENCBFR0dj0qRJffLVh3l5eVi6dCnOnz8PNzc3xd+PqD/jLWIi+sEuX76M6upqZGZmIiUlheFKBN4iJqJesH79ekyaNAlarRarVq1ydnOI+gXeIiYiIlIAZ7BEREQKYMASEREpgAFLRESkAAYsERGRAhiwRERECmDAEhERKYABS0REpAAGLBERkQL+H8UfGZs08zpqAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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Xn332mb755hu98soraty4sVUzc+ZMzZo1S3PnzlVmZqYiIyPVr18/FRcXWzXJyclauXKlli9frg0bNujYsWNKSEhQeXm5VTN8+HBlZ2crPT1d6enpys7OVmJi4qVsFwBQT/m488lfeukltWjRQgsXLrTGWrVqZf3ZGKM5c+Zo8uTJGjJkiCRp0aJFioiI0NKlSzV27Fg5nU698847ev/999W3b19J0uLFi9WiRQutW7dOAwYM0J49e5Senq7Nmzere/fukqQFCxYoPj5ee/fuVbt27S5d0wCAesetK9tVq1apS5cuuueeexQeHq7OnTtrwYIF1v79+/crLy9P/fv3t8b8/f3Vq1cvbdy4UZKUlZWlU6dOudRERUUpNjbWqtm0aZMcDocVtJLUo0cPORwOq+ZMpaWlKioqctkAAKgJt4bt999/r3nz5ikmJkarV6/WI488oqSkJL333nuSpLy8PElSRESEy3ERERHWvry8PPn5+alJkya/WRMeHl7l+cPDw62aM6Wmplqf7zocDrVo0eLimgUA1FtuDduKigpdf/31mj59ujp37qyxY8dq9OjRmjdvnkudl5eXy2NjTJWxM51Zc7b63zrPpEmT5HQ6rS03N/dC2wIAwIVbw7ZZs2bq0KGDy1j79u31ww8/SJIiIyMlqcrqs6CgwFrtRkZGqqysTIWFhb9Zk5+fX+X5Dx8+XGXVXMnf318hISEuGwAANeHWsL3hhhu0d+9el7HvvvtOLVu2lCRFR0crMjJSa9eutfaXlZUpIyNDPXv2lCTFxcXJ19fXpebQoUPavXu3VRMfHy+n06mtW7daNVu2bJHT6bRqAACwi1uvRn7iiSfUs2dPTZ8+XUOHDtXWrVs1f/58zZ8/X9Kvb/0mJydr+vTpiomJUUxMjKZPn67AwEANHz5ckuRwODRq1Cg9+eSTCgsLU2hoqJ566il17NjRujq5ffv2uu222zR69Gi99dZbkqQxY8YoISGBK5EBALZza9h27dpVK1eu1KRJkzRt2jRFR0drzpw5GjFihFXzzDPPqKSkRI899pgKCwvVvXt3rVmzRsHBwVbN7Nmz5ePjo6FDh6qkpER9+vRRWlqavL29rZolS5YoKSnJump50KBBmjt37qVrFgBQb3kZY4y7J1EXFBUVyeFwyOl01vjz28rPke96+RM1DAk9b/3Jol/016cTlJ+ff9arqQEAl1ZNs8DtP9cIAICnI2wBALAZYQsAgM0IWwAAbEbYAgBgM8IWAACbEbYAANiMsAUAwGaELQAANiNsAQCwGWELAIDNCFsAAGxWo7Bt3bq1jhw5UmX86NGjat269UVPCgAAT1KjsD1w4IDKy8urjJeWluqnn3666EkBAOBJqnU/21WrVll/Xr16tRwOh/W4vLxcn3/+uVq1alVrkwMAwBNUK2wHDx4sSfLy8tIDDzzgss/X11etWrXSK6+8UmuTAwDAE1QrbCsqKiRJ0dHRyszMVNOmTW2ZFAAAnqRaYVtp//79tT0PAAA8Vo3CVpI+//xzff755yooKLBWvJXefffdi54YAACeokZhO3XqVE2bNk1dunRRs2bN5OXlVdvzAgDAY9QobN98802lpaUpMTGxtucDAIDHqdH3bMvKytSzZ8/angsAAB6pRmH78MMPa+nSpbU9FwAAPFKN3kY+efKk5s+fr3Xr1qlTp07y9fV12T9r1qxamRwAAJ6gRmG7c+dOXXfddZKk3bt3u+zjYikAAFzVKGy/+OKL2p4HAAAei1vsAQBgsxqtbG+55ZbffLt4/fr1NZ4QAACepkZhW/l5baVTp04pOztbu3fvrnKDAgAA6rsahe3s2bPPOp6SkqJjx45d1IQAAPA0tfqZ7ciRI/ldZAAAzlCrYbtp0yY1bNiwNk8JAECdV6O3kYcMGeLy2BijQ4cOadu2bXrhhRdqZWIAAHiKGoWtw+FwedygQQO1a9dO06ZNU//+/WtlYgAAeIoahe3ChQtrex4AAHisGt88XpKysrK0Z88eeXl5qUOHDurcuXNtzQsAAI9Ro7AtKCjQfffdpy+//FKNGzeWMUZOp1O33HKLli9friuuuKK25wkAQJ1Vo6uRJ0yYoKKiIuXk5OiXX35RYWGhdu/eraKiIiUlJdX2HAEAqNNqtLJNT0/XunXr1L59e2usQ4cOev3117lACgCAM9RoZVtRUVHlHraS5Ovrq4qKioueFAAAnqRGYXvrrbfq8ccf188//2yN/fTTT3riiSfUp0+fWpscAACeoEZhO3fuXBUXF6tVq1Zq06aN2rZtq+joaBUXF+u1116r7TkCAFCn1egz2xYtWmj79u1au3atvv32Wxlj1KFDB/Xt27e25wcAQJ1XrZXt+vXr1aFDBxUVFUmS+vXrpwkTJigpKUldu3bVNddco6+++sqWiQIAUFdVK2znzJmj0aNHKyQkpMo+h8OhsWPHatasWbU2OQAAPEG1wvbrr7/Wbbfdds79/fv3V1ZW1kVPCgAAT1KtsM3Pzz/rV34q+fj46PDhwxc9KQAAPEm1wvZ3v/uddu3adc79O3fuVLNmzS56UgAAeJJqhe0dd9yhP/7xjzp58mSVfSUlJZoyZYoSEhJqbXIAAHiCan315/nnn9eKFSt01VVXafz48WrXrp28vLy0Z88evf766yovL9fkyZPtmisAAHVStcI2IiJCGzdu1KOPPqpJkybJGCNJ8vLy0oABA/TGG28oIiLClokCAFBXVftHLVq2bKlPP/1UhYWF+sc//iFjjGJiYtSkSRM75gcAQJ1Xo59rlKQmTZqoa9eu6tatW60EbWpqqry8vJScnGyNGWOUkpKiqKgoBQQEqHfv3srJyXE5rrS0VBMmTFDTpk0VFBSkQYMG6ccff3SpKSwsVGJiohwOhxwOhxITE3X06NGLnjMAABeixmFbmzIzMzV//nx16tTJZXzmzJmaNWuW5s6dq8zMTEVGRqpfv34qLi62apKTk7Vy5UotX75cGzZs0LFjx5SQkKDy8nKrZvjw4crOzlZ6errS09OVnZ2txMTES9YfAKB+c3vYHjt2TCNGjNCCBQtcVsjGGM2ZM0eTJ0/WkCFDFBsbq0WLFunEiRNaunSpJMnpdOqdd97RK6+8or59+6pz585avHixdu3apXXr1kmS9uzZo/T0dL399tuKj49XfHy8FixYoE8++UR79+51S88AgPrF7WE7btw4DRw4sMpNDPbv36+8vDyXm9H7+/urV69e2rhxoyQpKytLp06dcqmJiopSbGysVbNp0yY5HA51797dqunRo4ccDodVczalpaUqKipy2QAAqIka3fWntixfvlzbt29XZmZmlX15eXmSVOXq5oiICB08eNCq8fPzq/KZcUREhHV8Xl6ewsPDq5w/PDzcqjmb1NRUTZ06tXoNAQBwFm5b2ebm5urxxx/X4sWL1bBhw3PWeXl5uTw2xlQZO9OZNWerP995Jk2aJKfTaW25ubm/+ZwAAJyL28I2KytLBQUFiouLk4+Pj3x8fJSRkaFXX31VPj4+1or2zNVnQUGBtS8yMlJlZWUqLCz8zZr8/Pwqz3/48OHf/E6wv7+/QkJCXDYAAGrCbWHbp08f7dq1S9nZ2dbWpUsXjRgxQtnZ2WrdurUiIyO1du1a65iysjJlZGSoZ8+ekqS4uDj5+vq61Bw6dEi7d++2auLj4+V0OrV161arZsuWLXI6nVYNAAB2cttntsHBwYqNjXUZCwoKUlhYmDWenJys6dOnKyYmRjExMZo+fboCAwM1fPhwSb/eQ3fUqFF68sknFRYWptDQUD311FPq2LGjdcFV+/btddttt2n06NF66623JEljxoxRQkKC2rVrdwk7BgDUV269QOp8nnnmGZWUlOixxx5TYWGhunfvrjVr1ig4ONiqmT17tnx8fDR06FCVlJSoT58+SktLk7e3t1WzZMkSJSUlWVctDxo0SHPnzr3k/QAA6icvU/kDx/hNRUVFcjgccjqdNf78tvKz5Lte/kQNQ0LPW3+y6Bf99ekE5efnn/WKagDApVXTLHD792wBAPB0hC0AADYjbAEAsBlhCwCAzQhbAABsRtgCAGAzwhYAAJsRtgAA2IywBQDAZoQtAAA2I2wBALAZYQsAgM0IWwAAbEbYAgBgM8IWAACbEbYAANiMsAUAwGaELQAANiNsAQCwGWELAIDNCFsAAGxG2AIAYDPCFgAAmxG2AADYjLAFAMBmhC0AADYjbAEAsBlhCwCAzQhbAABsRtgCAGAzwhYAAJsRtgAA2IywBQDAZoQtAAA2I2wBALAZYQsAgM0IWwAAbEbYAgBgM8IWAACbEbYAANiMsAUAwGaELQAANiNsAQCwGWELAIDNCFsAAGxG2AIAYDPCFgAAmxG2AADYjLAFAMBmhC0AADYjbAEAsBlhCwCAzQhbAABs5tawTU1NVdeuXRUcHKzw8HANHjxYe/fudakxxiglJUVRUVEKCAhQ7969lZOT41JTWlqqCRMmqGnTpgoKCtKgQYP0448/utQUFhYqMTFRDodDDodDiYmJOnr0qN0tAgDg3rDNyMjQuHHjtHnzZq1du1anT59W//79dfz4catm5syZmjVrlubOnavMzExFRkaqX79+Ki4utmqSk5O1cuVKLV++XBs2bNCxY8eUkJCg8vJyq2b48OHKzs5Wenq60tPTlZ2drcTExEvaLwCgfvIyxhh3T6LS4cOHFR4eroyMDN18880yxigqKkrJycl69tlnJf26io2IiNBLL72ksWPHyul06oorrtD777+ve++9V5L0888/q0WLFvr00081YMAA7dmzRx06dNDmzZvVvXt3SdLmzZsVHx+vb7/9Vu3atTvv3IqKiuRwOOR0OhUSElKj/goKChQREaG7Xv5EDUNCz1t/sugX/fXpBOXn5ys8PLxGzwkAqD01zYLL6jNbp9MpSQoN/TWI9u/fr7y8PPXv39+q8ff3V69evbRx40ZJUlZWlk6dOuVSExUVpdjYWKtm06ZNcjgcVtBKUo8ePeRwOKyaM5WWlqqoqMhlAwCgJi6bsDXGaOLEibrxxhsVGxsrScrLy5MkRUREuNRGRERY+/Ly8uTn56cmTZr8Zs3ZVobh4eFWzZlSU1Otz3cdDodatGhxcQ0CAOqtyyZsx48fr507d2rZsmVV9nl5ebk8NsZUGTvTmTVnq/+t80yaNElOp9PacnNzL6QNAACquCzCdsKECVq1apW++OILNW/e3BqPjIyUpCqrz8rPPitrysrKVFhY+Js1+fn5VZ738OHDVVbNlfz9/RUSEuKyAQBQE24NW2OMxo8frxUrVmj9+vWKjo522R8dHa3IyEitXbvWGisrK1NGRoZ69uwpSYqLi5Ovr69LzaFDh7R7926rJj4+Xk6nU1u3brVqtmzZIqfTadUAAGAXH3c++bhx47R06VL99a9/VXBwsLWCdTgcCggIkJeXl5KTkzV9+nTFxMQoJiZG06dPV2BgoIYPH27Vjho1Sk8++aTCwsIUGhqqp556Sh07dlTfvn0lSe3bt9dtt92m0aNH66233pIkjRkzRgkJCRd0JTIAABfDrWE7b948SVLv3r1dxhcuXKgHH3xQkvTMM8+opKREjz32mAoLC9W9e3etWbNGwcHBVv3s2bPl4+OjoUOHqqSkRH369FFaWpq8vb2tmiVLligpKcm6annQoEGaO3euvQ0CAKDL7Hu2lzO+ZwsA8Ijv2QIA4IkIWwAAbEbYAgBgM8IWAACbEbYAANiMsAUAwGaELQAANiNsAQCwGWELAIDNCFsAAGxG2AIAYDPCFgAAmxG2AADYjLAFAMBmhC0AADYjbAEAsBlhCwCAzQhbAABsRtgCAGAzwhYAAJv5uHsCOL/Dhw9fcG1gYKAaNWpk42wAANVF2F7GTpeWSF4NFBsbe8HHNAkN0w8HDxC4AHAZIWwvY+WnSiVTob5/SFNQk6bnrS89dlTpU0fqxIkThC0AXEYI2zrAr1FjNQwJdfc0AAA1xAVSAADYjLAFAMBmhC0AADYjbAEAsBlhCwCAzQhbAABsRtgCAGAzwhYAAJsRtgAA2IywBQDAZoQtAAA2I2wBALAZYQsAgM0IWwAAbEbYAgBgM8IWAACbEbYAANiMsAUAwGaELQAANvNx9wRQ+w4fPnzBtYGBgWrUqJGNswEAELYe5HRpieTVQLGxsRd8TJPQMP1w8ACBCwA2Imw9SPmpUslUqO8f0hTUpOl560uPHVX61JE6ceIEYQsANiJsPZBfo8ZqGBLq7mkAAP4fF0gBAGAzwhYAAJsRtgAA2IzPbHHBXxXia0IAUDOEbT1W3a8K8TUhAKgZwrYeq85XhfiaEADUXL0K2zfeeEMvv/yyDh06pGuuuUZz5szRTTfd5O5puV11virEr1MBQPXVm7D94IMPlJycrDfeeEM33HCD3nrrLd1+++365ptvdOWVV7p7epe9mvw6VeMmodqetU1BQUEXVE84A/BU9SZsZ82apVGjRunhhx+WJM2ZM0erV6/WvHnzlJqa6ubZXf6q++tUx3/J07oZY9S6desLfo7qhnNFRYUaNLiwC+qrUysR/ABqV70I27KyMmVlZem5555zGe/fv782btx41mNKS0tVWlpqPXY6nZKkoqKiGs+juLhYknT8yM86dfLEeeuP/5InSTpx5JBUXubW+sra06UlFzT30mNHJVOh+LEvKbBxk/PWnzh6WJveer5a4Swvb8mU136tJEfjJvpbxpcXHPwA6obAwMCL+t91ZQYYY6p3oKkHfvrpJyPJ/P3vf3cZf/HFF81VV1111mOmTJliJLGxsbGxsVXZcnNzq5VD9WJlW8nLy8vlsTGmylilSZMmaeLEidbjiooK/fLLLwoLCzvnMedTVFSkFi1aKDc3VyEhITU6R11Dz/TsiepbvxI9V/ZsjFFxcbGioqKqda56EbZNmzaVt7e38vLyXMYLCgoUERFx1mP8/f3l7+/vMta4ceNamU9ISEi9+Y+1Ej3XD/Wt5/rWr0TPkuRwOKp9jnrxc41+fn6Ki4vT2rVrXcbXrl2rnj17umlWAID6ol6sbCVp4sSJSkxMVJcuXRQfH6/58+frhx9+0COPPOLuqQEAPFy9Cdt7771XR44c0bRp03To0CHFxsbq008/VcuWLS/ZHPz9/TVlypQqb097MnquH+pbz/WtX4meL5aXMdW9fhkAAFRHvfjMFgAAdyJsAQCwGWELAIDNCFsAAGxG2F5Cb7zxhqKjo9WwYUPFxcXpq6++cveUas3f/vY33XnnnYqKipKXl5c++ugjl/3GGKWkpCgqKkoBAQHq3bu3cnJy3DPZWpCamqquXbsqODhY4eHhGjx4sPbu3etS42k9z5s3T506dbK+4B8fH6/PPvvM2u9p/Z4pNTVVXl5eSk5OtsY8reeUlBR5eXm5bJGRkdZ+T+u30k8//aSRI0cqLCxMgYGBuu6665SVlWXtr42+CdtLpPIWf5MnT9aOHTt000036fbbb9cPP/zg7qnViuPHj+vaa6/V3Llzz7p/5syZmjVrlubOnavMzExFRkaqX79+1s0Z6pqMjAyNGzdOmzdv1tq1a3X69Gn1799fx48ft2o8refmzZtrxowZ2rZtm7Zt26Zbb71Vd911l/WPjqf1++8yMzM1f/58derUyWXcE3u+5pprdOjQIWvbtWuXtc8T+y0sLNQNN9wgX19fffbZZ/rmm2/0yiuvuPxiYK30Xc3f9EcNdevWzTzyyCMuY1dffbV57rnn3DQj+0gyK1eutB5XVFSYyMhIM2PGDGvs5MmTxuFwmDfffNMNM6x9BQUFRpLJyMgwxtSPno0xpkmTJubtt9/26H6Li4tNTEyMWbt2renVq5d5/PHHjTGe+Xc8ZcoUc+211551nyf2a4wxzz77rLnxxhvPub+2+mZlewlU3uKvf//+LuO/dYs/T7J//37l5eW59O/v769evXp5TP+Vt2AMDQ2V5Pk9l5eXa/ny5Tp+/Lji4+M9ut9x48Zp4MCB6tu3r8u4p/a8b98+RUVFKTo6Wvfdd5++//57SZ7b76pVq9SlSxfdc889Cg8PV+fOnbVgwQJrf231TdheAv/6179UXl5e5aYHERERVW6O4Ikqe/TU/o0xmjhxom688UbFxsZK8tyed+3apUaNGsnf31+PPPKIVq5cqQ4dOnhsv8uXL9f27duVmppaZZ8n9ty9e3e99957Wr16tRYsWKC8vDz17NlTR44c8ch+Jen777/XvHnzFBMTo9WrV+uRRx5RUlKS3nvvPUm19/dcb36u8XJQnVv8eSJP7X/8+PHauXOnNmzYUGWfp/Xcrl07ZWdn6+jRo/rwww/1wAMPKCMjw9rvSf3m5ubq8ccf15o1a9SwYcNz1nlSz7fffrv1544dOyo+Pl5t2rTRokWL1KNHD0me1a/06+1Tu3TpounTp0uSOnfurJycHM2bN0/333+/VXexfbOyvQRqcos/T1J5NaMn9j9hwgStWrVKX3zxhZo3b26Ne2rPfn5+atu2rbp06aLU1FRde+21+vOf/+yR/WZlZamgoEBxcXHy8fGRj4+PMjIy9Oqrr8rHx8fqy5N6PlNQUJA6duyoffv2eeTfsSQ1a9ZMHTp0cBlr3769dfFqbfVN2F4C9f0Wf9HR0YqMjHTpv6ysTBkZGXW2f2OMxo8frxUrVmj9+vWKjo522e+JPZ+NMUalpaUe2W+fPn20a9cuZWdnW1uXLl00YsQIZWdnq3Xr1h7X85lKS0u1Z88eNWvWzCP/jiXphhtuqPK1ve+++866SU2t9V2Di7dQA8uXLze+vr7mnXfeMd98841JTk42QUFB5sCBA+6eWq0oLi42O3bsMDt27DCSzKxZs8yOHTvMwYMHjTHGzJgxwzgcDrNixQqza9cuM2zYMNOsWTNTVFTk5pnXzKOPPmocDof58ssvzaFDh6ztxIkTVo2n9Txp0iTzt7/9zezfv9/s3LnT/OEPfzANGjQwa9asMcZ4Xr9n8+9XIxvjeT0/+eST5ssvvzTff/+92bx5s0lISDDBwcHWv1Oe1q8xxmzdutX4+PiYF1980ezbt88sWbLEBAYGmsWLF1s1tdE3YXsJvf7666Zly5bGz8/PXH/99dbXRDzBF198YSRV2R544AFjzK+Xz0+ZMsVERkYaf39/c/PNN5tdu3a5d9IX4Wy9SjILFy60ajyt54ceesj67/eKK64wffr0sYLWGM/r92zODFtP6/nee+81zZo1M76+viYqKsoMGTLE5OTkWPs9rd9KH3/8sYmNjTX+/v7m6quvNvPnz3fZXxt9c4s9AABsxme2AADYjLAFAMBmhC0AADYjbAEAsBlhCwCAzQhbAABsRtgCAGAzwhYAAJsRtgCq7cEHH9TgwYPdPQ2gzuAXpABUm9PplDFGjRs3dvdUgDqBsAXqkbKyMvn5+bl7GkC9w9vIQB3Wu3dvjR8/XuPHj1fjxo0VFham559/XpX/H7pVq1b6r//6Lz344INyOBwaPXq0JGnjxo26+eabFRAQoBYtWigpKUnHjx+XJE2aNMm6Ufi/69Spk6ZMmSKp6tvIpaWlSkpKUnh4uBo2bKgbb7xRmZmZ1v60tLQqq+CPPvrI5ebbX3/9tW655RYFBwcrJCREcXFx2rZtW628ToC7EbZAHbdo0SL5+Phoy5YtevXVVzV79my9/fbb1v6XX35ZsbGxysrK0gsvvKBdu3ZpwIABGjJkiHbu3KkPPvhAGzZs0Pjx4yVJI0aM0JYtW/TPf/7TOkdOTo527dqlESNGnHUOzzzzjD788EMtWrRI27dvV9u2bTVgwAD98ssvF9zHiBEj1Lx5c2VmZiorK0vPPfecfH19a/iqAJeZ2rg9EQD36NWrl2nfvr2pqKiwxp599lnTvn17Y4wxLVu2NIMHD3Y5JjEx0YwZM8Zl7KuvvjINGjQwJSUlxhhjOnXqZKZNm2btnzRpkunatav1+IEHHjB33XWXMcaYY8eOGV9fX7NkyRJrf1lZmYmKijIzZ840xhizcOFC43A4XJ5z5cqV5t//CQoODjZpaWnVfQmAOoGVLVDH9ejRw+Xt2Pj4eO3bt0/l5eWSpC5durjUZ2VlKS0tTY0aNbK2AQMGqKKiQvv375f06ypzyZIlkiRjjJYtW3bOVe0///lPnTp1SjfccIM15uvrq27dumnPnj0X3MfEiRP18MMPq2/fvpoxY4bLyhqo6whbwMMFBQW5PK6oqNDYsWOVnZ1tbV9//bX27dunNm3aSJKGDx+u7777Ttu3b9fGjRuVm5ur++6776znN///+fC/B37leOVYgwYNrLpKp06dcnmckpKinJwcDRw4UOvXr1eHDh20cuXKmjcOXEZ83D0BABdn8+bNVR7HxMTI29v7rPXXX3+9cnJy1LZt23Oes3nz5rr55pu1ZMkSlZSUqG/fvoqIiDhrbdu2beXn56cNGzZo+PDhkn4N0m3btik5OVmSdMUVV6i4uFjHjx+3wj87O7vKua666ipdddVVeuKJJzRs2DAtXLhQd9999/leAuCyx8oWqONyc3M1ceJE7d27V8uWLdNrr72mxx9//Jz1zz77rDZt2qRx48YpOztb+/bt06pVqzRhwgSXuhEjRmj58uX6n//5H40cOfKc5wsKCtKjjz6qp59+Wunp6frmm280evRonThxQqNGjZIkde/eXYGBgfrDH/6gf/zjH1q6dKnS0tKsc5SUlGj8+PH68ssvdfDgQf39739XZmam2rdvf3EvDnCZYGUL1HH333+/SkpK1K1bN3l7e2vChAkaM2bMOes7deqkjIwMTZ48WTfddJOMMWrTpo3uvfdel7p77rlHEyZMkLe393l/LWrGjBmqqKhQYmKiiouL1aVLF61evVpNmjSRJIWGhmrx4sV6+umnNX/+fPXt21cpKSnWPL29vXXkyBHdf//9ys/PV9OmTTVkyBBNnTr14l4c4DLBj1oAdVjv3r113XXXac6cOe6eCoDfwNvIAADYjLAFAMBmvI0MAIDNWNkCAGAzwhYAAJsRtgAA2IywBQDAZoQtAAA2I2wBALAZYQsAgM0IWwAAbPZ/SaxSaU0mYIIAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Check distributions of numeric columns\n",
+ "for i in range (len(numeric_cols)):\n",
+ " plt.figure(figsize=(5,4))\n",
+ " sns.histplot(data[numeric_cols[i]], bins=30)\n",
+ " plt.title(numeric_cols[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "a64255ad",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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8pn0MIMykpKRo0aJFior67QKoc04333yz1qxZE/AXZnR0tJKSkrR79+4aEZCdna377rtPU6dOrXFcY8rJyVFUVFTQsWVnZ2vWrFnyPM/fXttc6lL1fM45DR8+XCUlJUH3zc3NrfG4Tam+c6ocl6Ra17Mh61bbc3w4ONificZ8TkLydsY999yj1NRU/ysjI+OQHhjA4aW4uFgrV670b2/ZskWrVq2q8YJSXl6u4uLiGh8iLS8v16pVq7Ry5cqgxzWm1atX1zq2VatWacuWLQHba5tLXaqeb+XKlbUGhKSgj9uU6junynEdbD0bsm61PceHg7qew3B8Tur93RnTpk3TxIkT/duVVyIaU3x8vP/rQ307Q1Fh/I0nVcZ2OLydceDAAQ0bNizUw0CIpaSkqH///v7tbt26KTc3t0FXInJyctS/f/+gxzWmfv36yfO8oGPLyclRt27dAvavbS51qXq+jIwMJScnH/RKRPXHbUr1ndNJJ53kj6u29WzIutX2HB8ODvZnIlyfk3pfiYiPj1dKSkrAV2OrepkmISFBiYmJDfoKeDEO58tghzjPlvaVnJysq6++OoRPOMLBjBkz/LcypN/+vAf7sK3neSooKKhx2bZy/6ioqDo/pBsdHR3wWJXbKj8XUdexN910U61jGz9+fK1ja6iq54uKilJBQUGtY5owYUKzXt6vz5yio6P98R9sPeu7blX3P9zeypAO/mciXJ8TvjsDzWLUqFGHzbeyNrdWrer52Z+D6NChQ733jYlp+FW+Pn36KDs7u8b2rl27avTo0f5fjp7nafTo0crJyQm6vUuXLkGPq8rzPOXn5ys/Pz/g+Pz8/Brn7N27d43j8/Pz1aVLl1rHVjmG+syld+/eB71d/Xz9+vU76Jia28GeZ6nmuOp6zurzHB3sOT4cNPT3XagREWg2c+bMCfUQItITTzyhtm3bmo9PS0vTI4884v/LPSoq6qDne/zxx4Pen5aWFnT/qKgo3XnnnbWeLz8/3/85D+3bt9fo0aMPur2246rvG+z46ttmzpzp3w72OHWNoa65VD1/sNvBzjdz5syAF+127drV+bhNqeqcqo6rtvE3dN3q85wcbhr6+y6UiAg0Gz6Me3CxsbFKSkoKuAwfHx8f8OJe3aBBg9StWzdNnjw54DNFle+dep6nrKwseZ6npKQkDRo0KOCFICYmRlOmTFHHjh2Vn5+vqKgo5efna/LkyerYsWON/bOysnT00Udr8uTJSkpKChjHlClT1KZNG7Vp00Zjx45VUlKSfwWgth80Jf32lt7EiRPVsWNH3XTTTf7bkrVtD3bcxIkTNWnSpIB9gx1ffVubNm00adIkf9wTJ04MeJy6xlDXXCrPWdvtYOdr06aNxowZ46/ZpEmTQvq5qapzGjNmTK3PVbD961q3+j4nh5uG/r4LpbD6ORH8xMrIdqjrCwA4dPzESgAAEHJEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgAkRAQAATIgIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgAkRAQAATIgIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgAkRAQAATIgIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAExiQj2AqhISErR48WL/14gsrC8ARJawigjP85SYmBjqYaCJsL4AEFl4OwMAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgAkRAQAATIgIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgAkRAQAATIgIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgElMqAfQVLyKA3L13bn81+C/biJexYEmfwwAAJpaxEZE63X/MB2X/Mn8Rh4JAACRibczAACASURdiUhISNDixYsbfJxzTmVlZZKk+Ph4eZ7X2EOrVUJCQrM9FgAAjSmiIsLzPCUmJpqObdWqVSOPBgCAyMbbGQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBAABMiAgAAGBCRAAAABMiAgAAmBARAADAhIgAAAAmRAQAADAhIgAAgAkRAQAATIgIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMAkxnqgc06SVFxc3GiDAQAATavydbvydfxQmCOipKREkpSRkXHIgwAAAM2rpKREqamph3QOzxlTpKKiQj/88IOSk5Pled4hDaKq4uJiZWRk6LvvvlNKSkqjnTfcMM/Ic7jMlXlGFuYZWeozT+ecSkpK1LlzZ0VFHdqnGsxXIqKiotS1a9dDevCDSUlJieiFrsQ8I8/hMlfmGVmYZ2Spa56HegWiEh+sBAAAJkQEAAAwCbuIiI+PV0FBgeLj40M9lCbFPCPP4TJX5hlZmGdkae55mj9YCQAADm9hdyUCAAC0DEQEAAAwISIAAIAJEQEAAEzCLiIee+wx9ejRQwkJCcrJydH7778f6iHV24wZM+R5XsBXenq6f79zTjNmzFDnzp2VmJioQYMG6fPPPw84R1lZmW688Ua1b99eSUlJuuCCC/T9998391QCvPfeexo2bJg6d+4sz/O0aNGigPsba147duzQ2LFjlZqaqtTUVI0dO1Y7d+5s4tn9n7rmedlll9VY35NPPjlgn5Ywz3vuuUe5ublKTk7WEUccoQsvvFBfffVVwD6RsKb1mWckrOnjjz+uPn36+D9cKC8vT4sXL/bvj4S1lOqeZySsZTD33HOPPM/ThAkT/G1htaYujMyfP9/Fxsa6p556ym3YsMGNHz/eJSUluW+//TbUQ6uXgoICd8IJJ7itW7f6X9u3b/fvv/fee11ycrJbsGCBW79+vbv00ktdp06dXHFxsb/PNddc47p06eKWLFni1qxZ404//XTXt29fd+DAgVBMyTnn3L///W932223uQULFjhJbuHChQH3N9a8zj33XJeVleUKCwtdYWGhy8rKckOHDm2uadY5z3Hjxrlzzz03YH2LiooC9mkJ8zznnHPcM8884z777DO3bt06d/7557tu3bq53bt3+/tEwprWZ56RsKavvfaae/PNN91XX33lvvrqK3frrbe62NhY99lnnznnImMt6zPPSFjL6j766COXmZnp+vTp48aPH+9vD6c1DauIOOmkk9w111wTsO3YY491t9xyS4hG1DAFBQWub9++Qe+rqKhw6enp7t577/W3lZaWutTUVPfEE08455zbuXOni42NdfPnz/f3+d///ueioqLcW2+91aRjr6/qL66NNa8NGzY4Se7DDz/091mxYoWT5L788ssmnlVNtUXE8OHDaz2mJc7TOee2b9/uJLnly5c75yJ3TavP07nIXdO0tDQ3d+7ciF3LSpXzdC7y1rKkpMQdffTRbsmSJe60007zIyLc1jRs3s7Yv3+/Vq9ercGDBwdsHzx4sAoLC0M0qobbuHGjOnfurB49emjkyJH65ptvJEmbNm3Stm3bAuYXHx+v0047zZ/f6tWr9euvvwbs07lzZ2VlZYXtc9BY81qxYoVSU1PVv39/f5+TTz5ZqampYTX3ZcuW6YgjjlCvXr105ZVXavv27f59LXWeu3btkiS1bdtWUuSuafV5VoqkNS0vL9f8+fO1Z88e5eXlRexaVp9npUhay+uvv17nn3++zjrrrIDt4bam5v+Aq7H9/PPPKi8vV8eOHQO2d+zYUdu2bQvRqBqmf//+ev7559WrVy/9+OOP+utf/6oBAwbo888/9+cQbH7ffvutJGnbtm2Ki4tTWlpajX3C9TlorHlt27ZNRxxxRI3zH3HEEWEz9yFDhuiSSy5R9+7dtWnTJk2fPl1nnHGGVq9erfj4+BY5T+ecJk6cqFNPPVVZWVmSInNNg81Tipw1Xb9+vfLy8lRaWqrWrVtr4cKFOv744/0Xg0hZy9rmKUXOWkrS/PnztWbNGq1atarGfeH25zNsIqJS9f9W3DnXqP/VeFMaMmSI/+vevXsrLy9PRx11lJ577jn/Az6W+bWE56Ax5hVs/3Ca+6WXXur/OisrS/369VP37t315ptvasSIEbUeF87zvOGGG/Tpp5/qgw8+qHFfJK1pbfOMlDU95phjtG7dOu3cuVMLFizQuHHjtHz58lrH11LXsrZ5Hn/88RGzlt99953Gjx+vt99+WwkJCbXuFy5rGjZvZ7Rv317R0dE1Cmj79u01iqulSEpKUu/evbVx40b/uzQONr/09HTt379fO3bsqHWfcNNY80pPT9ePP/5Y4/w//fRT2M69U6dO6t69uzZu3Cip5c3zxhtv1GuvvaalS5eqa9eu/vZIW9Pa5hlMS13TuLg49ezZU/369dM999yjvn37as6cORG3lrXNM5iWuparV6/W9u3blZOTo5iYGMXExGj58uX629/+ppiYGH8c4bKmYRMRcXFxysnJ0ZIlSwK2L1myRAMGDAjRqA5NWVmZvvjiC3Xq1Ek9evRQenp6wPz279+v5cuX+/PLyclRbGxswD5bt27VZ599FrbPQWPNKy8vT7t27dJHH33k77Ny5Urt2rUrbOdeVFSk7777Tp06dZLUcubpnNMNN9ygV199Ve+++6569OgRcH+krGld8wympa5pdc45lZWVRcxa1qZynsG01LU888wztX79eq1bt87/6tevn/Lz87Vu3TodeeSR4bWm9f4IZjOo/BbPefPmuQ0bNrgJEya4pKQkt3nz5lAPrV4mTZrkli1b5r755hv34YcfuqFDh7rk5GR//Pfee69LTU11r776qlu/fr0bNWpU0G/L6dq1q/vPf/7j1qxZ484444yQf4tnSUmJW7t2rVu7dq2T5B588EG3du1a/1tvG2te5557ruvTp49bsWKFW7Fihevdu3ezfmvVweZZUlLiJk2a5AoLC92mTZvc0qVLXV5enuvSpUuLm+e1117rUlNT3bJlywK+HW7v3r3+PpGwpnXNM1LWdNq0ae69995zmzZtcp9++qm79dZbXVRUlHv77bedc5GxlnXNM1LWsjZVvzvDufBa07CKCOece/TRR1337t1dXFycy87ODvh2rHBX+b26sbGxrnPnzm7EiBHu888/9++vqKhwBQUFLj093cXHx7uBAwe69evXB5xj37597oYbbnBt27Z1iYmJbujQoW7Lli3NPZUAS5cudZJqfI0bN84513jzKioqcvn5+S45OdklJye7/Px8t2PHjmaa5cHnuXfvXjd48GDXoUMHFxsb67p16+bGjRtXYw4tYZ7B5ijJPfPMM/4+kbCmdc0zUtb08ssv9//O7NChgzvzzDP9gHAuMtbSuYPPM1LWsjbVIyKc1pT/ChwAAJiEzWciAABAy0JEAAAAEyICAACYEBEAAMCEiAAAACZEBAAAMCEiAACACREBtGCDBg3ShAkTQvLYy5Ytk+d52rlzZ0geH0DoEREA6hQsVgYMGKCtW7cqNTU1NIMCEHJEBHAY+/XXX83HxsXFKT09PWz+q3YAzY+IAFqIPXv26E9/+pNat26tTp06afbs2QH3e56nRYsWBWxr06aNnn32WUnS5s2b5XmeXn75ZQ0aNEgJCQl64YUXVFRUpFGjRqlr165q1aqVevfurX/84x/+OS677DItX75cc+bMked58jxPmzdvDvp2xoIFC3TCCScoPj5emZmZNcaYmZmpu+++W5dffrmSk5PVrVs3Pfnkk436PAFoPkQE0EJMmTJFS5cu1cKFC/X2229r2bJlWr16dYPPM3XqVP3lL3/RF198oXPOOUelpaXKycnRG2+8oc8++0xXXXWVxo4dq5UrV0qS5syZo7y8PF155ZXaunWrtm7dqoyMjBrnXb16tf74xz9q5MiRWr9+vWbMmKHp06f7EVNp9uzZ6tevn9auXavrrrtO1157rb788kvTcwIgtGJCPQAAddu9e7fmzZun559/XmeffbYk6bnnnlPXrl0bfK4JEyZoxIgRAdsmT57s//rGG2/UW2+9pVdeeUX9+/dXamqq4uLi1KpVK6Wnp9d63gcffFBnnnmmpk+fLknq1auXNmzYoPvvv1+XXXaZv995552n6667TtJvQfPQQw9p2bJlOvbYYxs8FwChxZUIoAX473//q/379ysvL8/f1rZtWx1zzDENPle/fv0CbpeXl+uuu+5Snz591K5dO7Vu3Vpvv/22tmzZ0qDzfvHFFzrllFMCtp1yyinauHGjysvL/W19+vTxf+15ntLT07V9+/YGzwNA6HElAmgBnHN17uN5Xo39gn1wMikpKeD27Nmz9dBDD+nhhx9W7969lZSUpAkTJmj//v0NHmP1D1kGG3dsbGyNcVdUVDTosQCEB65EAC1Az549FRsbqw8//NDftmPHDn399df+7Q4dOmjr1q3+7Y0bN2rv3r11nvv999/X8OHDNWbMGPXt21dHHnmkNm7cGLBPXFxcwNWEYI4//nh98MEHAdsKCwvVq1cvRUdH1zkOAC0PVyKAFqB169a64oorNGXKFLVr104dO3bUbbfdpqio//t3wBlnnKFHHnlEJ598sioqKjR16tQa/+oPpmfPnlqwYIEKCwuVlpamBx98UNu2bdNxxx3n75OZmamVK1dq8+bNat26tdq2bVvjPJMmTVJubq5mzpypSy+9VCtWrNAjjzyixx57rHGeBABhhysRQAtx//33a+DAgbrgggt01lln6dRTT1VOTo5//+zZs5WRkaGBAwdq9OjRmjx5slq1alXneadPn67s7Gydc845GjRokNLT03XhhRcG7DN58mRFR0fr+OOPV4cOHYJ+XiI7O1svv/yy5s+fr6ysLN1+++268847Az5UCSCyeK4+b7YCAABUw5UIAABgQkQAAAATIgIAAJgQEQAAwISIAAAAJkQEAAAwISIAAIAJEQEAAEyICAAAYEJEAAAAEyICAACYEBEAAMDk/wOk0LnesWXJ2gAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Box plots for numeric columns\n",
+ "for i in range (len(numeric_cols)):\n",
+ " plt.figure()\n",
+ " plt.title(numeric_cols[i])\n",
+ " sns.boxplot(x=data[numeric_cols[i]], data=data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "51443624",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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0bds2NWvWTJK0bds2BQUF6ZdfflGdOnWuqa60tDTZ7XalpqbKw8Pjml4T+NzifPwEcDPsnPqfm76NY68G3PRt4NpUf2mPo0sAUEhc6/d3vi67f+211/T6668rISFBsbGx2rVrl/nIbxjKyspSZGSkLly4oKCgIB0+fFgJCQnq1KmT2cbV1VWtW7fWli1bJEk7d+7U5cuXc7Xx8fFRgwYNzDZbt26V3W43w5AkNW/eXHa73WwDAACsLV+nzJKTk9W7d+8CKWDPnj0KCgrSpUuXVLZsWUVFRalevXpmWPHy8srV3svLS0ePHpUkJSQkyMXFReXLl8/TJmdKgISEBHl6eubZrqenZ65pA/4uIyNDGRkZ5vO0tLT87SAAACj08tVD1Lt3b61evbpACqhTp45iY2O1bds2Pf300xo4cKD27dtnrv/r6Tjpz4HWf1/2d39vc7X2//Y+ERER5iBsu90uX1/fa90lAABQxOSrh+i2227Tiy++qG3btikgIEDOzs651g8fPvya38vFxcUcVN2kSRPt2LFDb775pjluKCEhQVWqVDHbJyYmmr1G3t7eyszMVHJycq5eosTERLVo0cJsc+rUqTzbTUpKytP79Ffjx4/XqFGjzOdpaWmEIgAAiql8BaL33ntPZcuWVUxMjGJiYnKts9ls1xWI/s4wDGVkZMjf31/e3t5as2aN7rzzTklSZmamYmJiNHnyZElSYGCgnJ2dtWbNGvXp00eSFB8fr7i4OE2ZMkWSFBQUpNTUVP3www+6++67JUnbt29XamqqGZquxtXVVa6urvneDwAAUHTkKxAdPny4QDb+/PPPq2vXrvL19dW5c+cUGRmpjRs3Kjo6WjabTaGhoZo0aZJq166t2rVra9KkSSpTpoz69esnSbLb7Ro0aJDCwsJUsWJFVahQQaNHj1ZAQIA6dOggSapbt666dOmiIUOGaN68eZL+vOy+W7du13yFGQAAKN7yPQ9RQTh16pSCg4MVHx8vu92uhg0bKjo6Wh07dpQkjRkzRunp6QoJCVFycrKaNWum1atXm3MQSdLMmTPl5OSkPn36KD09Xe3bt9fChQtzTRL50Ucfafjw4ebVaD169NDs2bNv7c4CAIBCK1/zED3xxBP/uP7DDz/Md0GFFfMQFW3MQ2QtzEMEIMe1fn/n+7L7v7p8+bLi4uKUkpJy1Zu+AgAAFGb5CkRRUVF5lmVnZyskJEQ1a9a84aIAAABupXzNQ3TVNypRQiNHjtTMmTML6i0BAABuiQILRJJ06NAhXblypSDfEgAA4KbL1ymzv05YKP05d1B8fLy++eYbDRw4sEAKAwAAuFXyFYh27dqV63mJEiVUuXJlTZ8+/V+vQAMAAChs8hWINmzYUNB1AAAAOMwNTcyYlJSkAwcOyGaz6fbbb1flypULqi4AAIBbJl+Dqi9cuKAnnnhCVapUUatWrXTvvffKx8dHgwYN0sWLFwu6RgAAgJsqX4Fo1KhRiomJ0YoVK5SSkqKUlBR9+eWXiomJUVhYWEHXCAAAcFPl65TZ559/rs8++0xt2rQxl913330qXbq0+vTpo7lz5xZUfQAAADddvnqILl68KC8vrzzLPT09OWUGAACKnHwFoqCgIL388su6dOmSuSw9PV2vvPKKgoKCCqw4AACAWyFfp8xmzZqlrl27qlq1amrUqJFsNptiY2Pl6uqq1atXF3SNAAAAN1W+AlFAQIAOHjyopUuX6pdffpFhGOrbt6/69++v0qVLF3SNAAAAN1W+AlFERIS8vLw0ZMiQXMs//PBDJSUlaezYsQVSHAAAwK2QrzFE8+bN0x133JFnef369fXuu+/ecFEAAAC3Ur4CUUJCgqpUqZJneeXKlRUfH3/DRQEAANxK+QpEvr6+2rx5c57lmzdvlo+Pzw0XBQAAcCvlawzR4MGDFRoaqsuXL6tdu3aSpHXr1mnMmDHMVA0AAIqcfAWiMWPG6OzZswoJCVFmZqYkqVSpUho7dqzGjx9foAUCAADcbPkKRDabTZMnT9aLL76o/fv3q3Tp0qpdu7ZcXV0Luj4AAICbLl+BKEfZsmXVtGnTgqoFAADAIfI1qBoAAKA4IRABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLc2ggioiIUNOmTeXu7i5PT0898MADOnDgQK42hmEoPDxcPj4+Kl26tNq0aaO9e/fmapORkaFhw4apUqVKcnNzU48ePXTixIlcbZKTkxUcHCy73S673a7g4GClpKTc7F0EAABFgEMDUUxMjJ555hlt27ZNa9as0ZUrV9SpUydduHDBbDNlyhTNmDFDs2fP1o4dO+Tt7a2OHTvq3LlzZpvQ0FBFRUUpMjJSmzZt0vnz59WtWzdlZWWZbfr166fY2FhFR0crOjpasbGxCg4OvqX7CwAACiebYRiGo4vIkZSUJE9PT8XExKhVq1YyDEM+Pj4KDQ3V2LFjJf3ZG+Tl5aXJkyfrySefVGpqqipXrqwlS5bokUcekSSdPHlSvr6+WrlypTp37qz9+/erXr162rZtm5o1ayZJ2rZtm4KCgvTLL7+oTp06/1pbWlqa7Ha7UlNT5eHhcU37E/jc4nz+JFDQdk79z03fxrFXA276NnBtqr+0x9ElACgkrvX7u1CNIUpNTZUkVahQQZJ0+PBhJSQkqFOnTmYbV1dXtW7dWlu2bJEk7dy5U5cvX87VxsfHRw0aNDDbbN26VXa73QxDktS8eXPZ7Xazzd9lZGQoLS0t1wMAABRPhSYQGYahUaNGqWXLlmrQoIEkKSEhQZLk5eWVq62Xl5e5LiEhQS4uLipfvvw/tvH09MyzTU9PT7PN30VERJjjjex2u3x9fW9sBwEAQKFVaALRs88+q927d+u///1vnnU2my3Xc8Mw8iz7u7+3uVr7f3qf8ePHKzU11XwcP378WnYDAAAUQYUiEA0bNkxfffWVNmzYoGrVqpnLvb29JSlPL05iYqLZa+Tt7a3MzEwlJyf/Y5tTp07l2W5SUlKe3qccrq6u8vDwyPUAAADFk0MDkWEYevbZZ/XFF19o/fr18vf3z7Xe399f3t7eWrNmjbksMzNTMTExatGihSQpMDBQzs7OudrEx8crLi7ObBMUFKTU1FT98MMPZpvt27crNTXVbAMAAKzLyZEbf+aZZ7Rs2TJ9+eWXcnd3N3uC7Ha7SpcuLZvNptDQUE2aNEm1a9dW7dq1NWnSJJUpU0b9+vUz2w4aNEhhYWGqWLGiKlSooNGjRysgIEAdOnSQJNWtW1ddunTRkCFDNG/ePEnS0KFD1a1bt2u6wgwAABRvDg1Ec+fOlSS1adMm1/IFCxbosccekySNGTNG6enpCgkJUXJyspo1a6bVq1fL3d3dbD9z5kw5OTmpT58+Sk9PV/v27bVw4UKVLFnSbPPRRx9p+PDh5tVoPXr00OzZs2/uDgIAgCKhUM1DVJgxD1HRxjxE1sI8RAByFMl5iAAAAByBQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACzPydEFAEBRdM/b9zi6BPx/m4dtdnQJKAboIQIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbn0ED03XffqXv37vLx8ZHNZtPy5ctzrTcMQ+Hh4fLx8VHp0qXVpk0b7d27N1ebjIwMDRs2TJUqVZKbm5t69OihEydO5GqTnJys4OBg2e122e12BQcHKyUl5SbvHQAAKCocGoguXLigRo0aafbs2VddP2XKFM2YMUOzZ8/Wjh075O3trY4dO+rcuXNmm9DQUEVFRSkyMlKbNm3S+fPn1a1bN2VlZZlt+vXrp9jYWEVHRys6OlqxsbEKDg6+6fsHAACKBidHbrxr167q2rXrVdcZhqFZs2bphRdeUK9evSRJixYtkpeXl5YtW6Ynn3xSqamp+uCDD7RkyRJ16NBBkrR06VL5+vpq7dq16ty5s/bv36/o6Ght27ZNzZo1kyS9//77CgoK0oEDB1SnTp1bs7MAAKDQKrRjiA4fPqyEhAR16tTJXObq6qrWrVtry5YtkqSdO3fq8uXLudr4+PioQYMGZputW7fKbrebYUiSmjdvLrvdbra5moyMDKWlpeV6AACA4qnQBqKEhARJkpeXV67lXl5e5rqEhAS5uLiofPny/9jG09Mzz/t7enqaba4mIiLCHHNkt9vl6+t7Q/sDAAAKr0IbiHLYbLZczw3DyLPs7/7e5mrt/+19xo8fr9TUVPNx/Pjx66wcAAAUFYU2EHl7e0tSnl6cxMREs9fI29tbmZmZSk5O/sc2p06dyvP+SUlJeXqf/srV1VUeHh65HgAAoHgqtIHI399f3t7eWrNmjbksMzNTMTExatGihSQpMDBQzs7OudrEx8crLi7ObBMUFKTU1FT98MMPZpvt27crNTXVbAMAAKzNoVeZnT9/Xr/99pv5/PDhw4qNjVWFChVUvXp1hYaGatKkSapdu7Zq166tSZMmqUyZMurXr58kyW63a9CgQQoLC1PFihVVoUIFjR49WgEBAeZVZ3Xr1lWXLl00ZMgQzZs3T5I0dOhQdevWjSvMAACAJAcHoh9//FFt27Y1n48aNUqSNHDgQC1cuFBjxoxRenq6QkJClJycrGbNmmn16tVyd3c3XzNz5kw5OTmpT58+Sk9PV/v27bVw4UKVLFnSbPPRRx9p+PDh5tVoPXr0+J9zHwEAAOuxGYZhOLqIoiAtLU12u12pqanXPJ4o8LnFN7kqXKudU/9z07dx7NWAm74NXJvqL+256du45+17bvo2cG02D9vs6BJQiF3r93ehHUMEAABwqxCIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5Tk5ugAAAAq7mFatHV0C/r/W38XclPelhwgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFiepQLRnDlz5O/vr1KlSikwMFDff/+9o0sCAACFgGUC0ccff6zQ0FC98MIL2rVrl+6991517dpVx44dc3RpAADAwSwTiGbMmKFBgwZp8ODBqlu3rmbNmiVfX1/NnTvX0aUBAAAHs0QgyszM1M6dO9WpU6dcyzt16qQtW7Y4qCoAAFBYODm6gFvh9OnTysrKkpeXV67lXl5eSkhIuOprMjIylJGRYT5PTU2VJKWlpV3zdrMy0vNRLW6G6zlu+XXuUtZN3wauza043lfSr9z0beDa3IrjfeEKx7uwuN7jndPeMIx/bGeJQJTDZrPlem4YRp5lOSIiIvTKK6/kWe7r63tTasPNZX/7KUeXgFspwu7oCnAL2cdyvC3Fnr/jfe7cOdn/4bWWCESVKlVSyZIl8/QGJSYm5uk1yjF+/HiNGjXKfJ6dna2zZ8+qYsWK/zNEFUdpaWny9fXV8ePH5eHh4ehycJNxvK2F420tVj3ehmHo3Llz8vHx+cd2lghELi4uCgwM1Jo1a/Tggw+ay9esWaOePXte9TWurq5ydXXNtaxcuXI3s8xCzcPDw1IfIKvjeFsLx9tarHi8/6lnKIclApEkjRo1SsHBwWrSpImCgoL03nvv6dixY3rqKU6lAABgdZYJRI888ojOnDmjV199VfHx8WrQoIFWrlwpPz8/R5cGAAAczDKBSJJCQkIUEhLi6DKKFFdXV7388st5Th+ieOJ4WwvH21o43v/MZvzbdWgAAADFnCUmZgQAAPgnBCIAAGB5BCIAAGB5BCIAAGB5BCIUqPPnz0v693vGoOji2AIojghEuGE5X5Dfffedpk6dKinvfeNQdGVnZ0uSli9fLoljazU5xx8o7ghEuGE5X5DHjh3TxIkT9dxzz5l3F+aXadFXokQJnTlzRn369NHgwYN15swZSRxbKzh79qxKlOBrwqqys7Mt1SPM/3QUmAEDBigyMlKbNm3SJ598Ikn8Mi0mKlasqBUrVmjXrl2aP3++JI5tcbd79255eXnpm2++kSRlZWU5uCLcLDmhJysrS6mpqdq8ebOSkpJUokQJ2Ww2GYZhiWBkqZmqcfNkZWXJZrPpgQce0K+//qqQkBAdOXJE48aNU9myZWUYBqdairjOnTvr5MmTGj58uE6cOKGJEyeqXLlyys7OJhwVQ9WrV9djjz2mt956S3Xr1lXNmjUdXRJukuzsbJUsWVLTpk3T4sWLdeXKFTk5Oally5YKDQ1V3bp1HV3iLcFM1ci3fwo5S5cu1fz58zVy5Ej17NnzFleGm+mLL77Qa6+9psGDB3MrnGLu6NGjeuKJJ/Tbb7/pgw8+UIcOHZSVlaWSJUs6ujQUsNOnT8vX11dvvPGGKlWqpBMnTujrr7/W4cOH1bp1az399NNq2bKlo8u8qeghwg179913tW/fPrVq1UqJiYmqVauWatWqpStXrqhXr1568803NXToUDk7O0tiUG5RkBN2r1y5ok2bNsnLy0u+vr5KSkpSr1699Mcff2jYsGE6c+aMwsLCVKZMGXoBiyE/Pz99/fXXGjx4sKZNm6aGDRvK09PT0WWhAOV8bmNjY9WjRw+NGDFCkpSRkaEuXbro+++/1zfffKP27dtr06ZNatq0qYMrvnkIRMiXnA/R6dOntXnzZp04cUKrVq1S9erVFRsbq+rVq6tKlSoyDEPTpk2Tn5+funfv7uiycZ3efPNNzZs3TykpKXJxcVGtWrV09OhRtWzZUp6ennr99ddVo0YNBQcHE4aKuJxTn1lZWUpJSVHFihUl/XlD0Hnz5qlHjx5q06aNFi9erCZNmtBTVEzYbDZlZWVp8+bNOn/+vNLT01W6dGm5urqqUaNGCggIUPv27bV///5iHYYkTpkhn3J+GY4dO1ZOTk56/fXXlZWVpVOnTsnHx0c7d+6Uq6urnJ2dNXv2bH344Yf64osv1LlzZ0eXjn/x196hiIgI+fv7695771VsbKwuXbokm82mXbt2ycfHR/v379fChQsVERFh/mWJoinnuL/44ovatm2bzp49q5o1a6py5cpycXFR+fLlNWPGDN1zzz36+OOP5e7u7uiSUUC+/fZbPf744zp37pxCQ0MVFhamcuXKObqsW45AhHy7ePGibr/9dn3xxRdq2rTpP/YQDBgwQBUqVNCsWbMYgFvI5fQUREREaO3atVq3bt0/tn/jjTcUHR2tTz75hNMpRVROGLp48aJeeukl2Ww2+fn5aceOHXJ1ddWFCxf0yy+/yMfHR3FxcfLw8NCyZctUv359R5eOAnD+/HlFR0dr9erVWr9+vTw8PNS9e3c9+eST8vHxcXR5twynzHDdcr4w9+7dq1atWsnd3d3sds25TDNHztUKKSkpOnnyJGGoCMg5RmfOnFHXrl0l/d98JCVLlsxzVdmjjz6q559/XidOnCAQFVE5VxlNmDBBrq6uioiIMNflfIYl6dy5czp9+rQGDRqkjz/+WK+++ipjx4qonF7+7du368qVK3r44YfVunVrbdu2TRs3blR0dLS+/PJL3XnnnZoxY4bKly/v6JJvOr6dcN1yvgxnzZql9evX67///a8kqWTJknnmrMj5Rfroo49qzpw5jikY1yxnrpktW7YoNTVV27dv18WLF1WiRAlzvMjfQ21SUpJ69Oihu+6665bXi4KRc2y3bdumRo0aSfrz/0JWVpacnJzM/xfu7u7y9/fXwIEDNXfuXJ06dYowVETlHPN58+Zp+PDhiouLU+XKldW9e3e99NJLioiIUNeuXRUfHy+73e7gam8NeoiQLwkJCbLZbKpYsaLee+89nT59Wt27d9e9996rsmXL5mnfv39/B1SJ65XzS3Lx4sX64IMPJEn169dXcHCw/Pz8zID7V3Xr1tWHH354S+tEwcnpAfrtt9/Utm1bnT59WpJyDZjO+XdOb5BhGKpbt668vLwcUjMKziuvvKLQ0FA1adJEzz//vEaMGCG73a527dqpRYsWOn36tGV69hlDhBvyyy+/aOHChdq4caNKlCihu+66S61atVLHjh0t0cVanP3xxx96+eWXtWDBAtWqVUtDhgxRz5495efnJ1dXV0eXhwJ2xx136Ndff1VAQICWLFmihg0b/s+2sbGxKleunGrUqHHrCkSBy8zMlIuLiyRp9uzZioqKUps2bRQSEmJeZWglBCLk21/HDvz+++9asmSJNm7cqCNHjmju3Lnq0qWLgytEfvx9jFBycrLCw8M1f/58lS1bVoMHD9bEiRMt81ejVcTHx+vjjz/WlClTlJ6erqFDh2ro0KGqVauWo0tDAcoZO/Tzzz8rMjJSnp6eateunapWraq33npLCxcuVJcuXTRp0iRVqlTJ0eXeUgQiXLcDBw4oOjpaLi4uqlixYq7eoMTEREVGRiokJOSqp1dQOP013MbHxyszM1OS5OHhYR7brKwsjRs3TpmZmXrzzTcZTFuM/PVYnjt3TvPnz9ebb76ps2fPqnfv3ho5cqQaNGjg4CpRkIYNG6Z33nlHd955p44dO6ZatWqpc+fOWrt2rbZu3aq6detqx44dKlOmjKNLvWUIRLgmOX9VrF69WhMmTFB8fLxq166tI0eOqH79+uratasefPBBValSxXwNX5hFR06v0LRp0/Tpp5/qp59+UsOGDdWlSxe1bt1ajRo1yjNehHuYFW1//XxmZGTkOQ165coVLVu2TKNHj9bgwYM1adIkR5SJmyQuLk7Tpk1TZmampk6dqvj4eP3444/Kzs7Wp59+qlq1apk3crYKAhGuS+PGjdWxY0dNnTpVL7zwgpYuXWr+JREQEKC2bdvqxRdf5IuyCMkJNnFxcWrVqpVeffVV9e/fXz4+PuYA+b59+6pFixbq0KGDKleu7OCKUZC++OILffTRR/r555/10EMPqWvXrmrUqFGuMYCXL1+Ws7MzIbiY+eOPPzRu3Dh9/fXXmjVrlgYMGGAOoLfiTOT8z8a/ysnMP/74o1JSUvTSSy9J+vMGrm+++aZWrVqlgIAAxcfHq1KlSipRooTI2UXPpEmT9MADD+jZZ5/V5s2bVaVKFR08eFB9+/bVe++9p7CwMKWnpzu6TBSAv06vMGLECFWuXFkjR47U1KlT1a1bNw0aNEirVq3SH3/8IUnmfQgJQ0VXzu/kxMREvfTSS9q9e7fKlCmjBQsW6JNPPtHWrVv17rvvmv83rBaGJC67x7/461+Ehw4dUtOmTeXk5KTPPvtM5cuXV8uWLWWz2dSzZ08lJiZq4MCBDq4Y1yrnF2SJEiV0/vx5nTp1Sk8//bQkacqUKQoODla5cuU0YMAAnThxQgMGDFD16tU5FVoM5HymX3zxRfXu3VszZszQJ598ovr16ys8PFxhYWF65JFH1LhxY61atUpubm4Orhg3Kuczu3nzZn3yySeaPHmy7rjjDlWvXl0XLlzQuXPnFBUVpbi4OL3zzjuWDL8EIvyjnA+FYRjq1auX6tSpI1dXV6WlpalUqVLm4NtDhw7pwoUL5ikWvjALv78eI5vNpmeffVb+/v5KSkrSpUuXFBAQIElq1KiRTpw4wcSLxYjNZtOJEyf0xx9/aNq0aZKk8PBwPfnkk3rooYf022+/afXq1Wrfvr3c3NwIwcVI+/btFRcXp9TUVEVFRen06dNKSkrShg0blJSUpJ9++smSYUgiEOEfBAQEKDg4WKGhoXJxcZGzs7MaNGigEiVKqHnz5nrjjTcUHh6uK1euKCoqSitXrpTEYNui4JtvvlFSUpIee+wxSZKbm5s6duwom80mNzc3Va1aVStXrlTVqlUVGRmps2fPyt/fXxJht7jIyMhQ//79ValSJf3000+y2Wzq2LGjJOm+++7Tr7/+qrCwMElcIFHU/fX4eXh4KD4+XlWqVNHgwYMl/d94oV9//VWlS5d2ZKkOxbcWriolJUX33nuvZs2aJU9PT73wwgtKSUkxL6WvUaOGnnnmGf32229KSEjQCy+8oKCgIBmGQRgqAjZu3JirCz0hIUFly5aVm5ubsrOz1blzZ0VFRal79+6KiYnRzJkzJf155RGKh1q1aunRRx9VpUqVVKFCBTk5OenAgQO6ePGili5dql27dplXnvGZLrpywlB2drbmzp2r1q1bq1u3bgoMDMx12yVJuv322+Xr6+vIch2Kq8zwP12+fFnx8fGKiorS6NGjVbZsWf3nP//R6NGjzQ9NYmKi3N3dzb8q6B0qGpKSksyrxe69914lJibqoYce0tChQ83Zh1NSUrR+/Xrdc8893KKhGLlab09qaqp69eqlH3/8UdWrV1dSUpIWLVqkzp07W/Jqo+Ik59YskyZN0n//+18FBgbq7rvv1o8//qjPPvtMTZo00YIFC+Tn52f5nkACEa4q54ORnp6u8PBwbd68WdKfl2keP35cDz/8sF5++WXVrVvXwZXiRm3cuFGrV6/Wxo0blZKSolatWmnIkCEKDAx0dGkoQH/tKTh69Kh++eUX2e12tWjRwmzz/vvva+/everTp0+u5Sj6PD099dZbb6lv376SpAsXLmjr1q0aPXq0evfurRdeeMHBFToegQhXlfNXRVhYmHbv3q358+ercuXKSkpK0qpVqxQeHq7ExETdfffd+uabbyx535viYNSoUZo8ebIyMzP1008/acOGDdqwYYMSExNVr149hYWFqXnz5o4uEwUg5zM9a9YszZw5U9nZ2fL09FSFChXUr18/PfbYY5buHSjO9u3bpz59+mjRokV5/tB54YUXFBMTo6ioKMvPMca5DVxVzlihb775Rvfff7/8/PxUpkwZ+fn56amnntKYMWNUq1YtNW7cmDBURGRnZ0v684pASXr33Xe1bNkyOTs7y83NTffee6/Gjx+vqVOn6oknntChQ4e0e/duR5aMApTzmQ4PD9crr7yiJUuWKCQkRF5eXpo+fbpatWqlqVOnMtdUMVStWjUZhqGIiAhduHAh17rmzZvr+PHj/B4XPUT4B1lZWXr88ceVkJCg1atX51r3xx9/KCQkRDNmzFCtWrUYO1REXLx4UV5eXnrooYe0cuVKjRs3TqNGjbpq26SkJFWsWNGcaJPeg6Lv999/17hx47RgwQJzbqHjx49ry5YtWr9+vVasWKF33nlHDz74oIMrRUH79ttvNWbMGLVq1UpdunRR48aNlZiYqKefflpNmzbV22+/7egSHY5vMPxPJUuW1MCBAxUXF6chQ4bo+++/N9etW7dOW7duNe+ETRgq/LKzs1WmTBktW7ZMmzdv1unTp7VhwwatWLFCSUlJZg+SJI0ZM0aHDx82jythqHioWbOmsrKytG3bNnOZr6+vHnnkEb366qv68MMPCUPFVJs2bTR06FBt2bJF48ePV9u2bdWyZUtVq1ZNr7/+uqPLKxToIcK/mjNnjv773/+qZMmSKlmypJKTk5WcnKwnnnhCL774ojk2AUVH9+7dVbNmTcXGxur777/Xvffeq5CQEDVr1kynTp1SUFCQTpw4IR8fH0eXihuU8/n87rvvtHTpUm3YsEElSpTQG2+8QfgppnJ67K9cuaLff/9dly5dUsOGDSVJly5d0ldffaXSpUvLw8NDgYGB5oS6VkcgwlVlZmbK2dnZ7BnYu3evvvzyS505c0apqanq27ev2rVrx+mUIiTnl+R3332n8PBwRUdHy8XFRT///LMmTJigVatWqWbNmnJxcVHTpk21YMECLrkuRm6//XZVr15dDRo00NGjR/Xdd9+pSpUqCgsL0+OPP+7o8lCAcj7rI0aM0Lp167Rv3z75+vrqscce06BBg1S9enVHl1goEYhgyvnyi42N1dy5cxUVFaW7775bvXv31n333Wf5KxCKi6FDh8rNzc280ijntNjx48f14Ycfql69eurUqZPsdjtjw4qJ3bt3q2/fvoqLi1OJEiWUmJioXbt26fPPP1d0dLSSkpIUHR2t1q1bO7pU3KCcP1B37dqlli1b6u2331bTpk0VFRWlt99+W5cvX9agQYPUv39/BQQEmDfuBYEIV9GoUSN5eXmpQ4cO+u677/Tdd9/pjjvuUHBwsNq0aaPbbrvN0tO7F0U5wSYtLU2vvfaaypcvr/Hjx0v6szewRIkSnPYshnK+HHfs2KEFCxZo+PDhuuOOO8z1ycnJ2r17t7788ktNmDBBFSpUcGC1KEhvv/22fvnlF73zzjvmsqysLM2bN09Tp07V0aNHdfz4cVWtWtWBVRYuBCLksm/fPj388MOKiYkxe4QOHjyoSZMm6euvv5azs7Pmz5+v++67z8GVIj8mT56sOXPmyM3NTcuWLVPjxo3NdZcvX1bJkiXpESpmfvnlF9WrV0+SFBERobFjx+Zpc+nSJZUqVYrT30VczvFLTk7W559/rrVr1+qdd95RxYoV8/T2xsTE0CP4N/zmg6T/m6MmIyND99xzjzlXhWEYql27thYsWKC4uDj16tVL9evXN9ehaKlataqaNGmilJQUhYSE6I033tBPP/0kSXJ2diYMFQOGYejy5cvmcz8/P33zzTd6/PHH9eKLL6phw4aKjIzM9ZpSpUpJ4mrCoi7n+C1ZskRDhw7VJ598osjISJ07d878bOf83iYM5UUPEUyHDh1S7dq1JUkTJ07U008/bXah85dj8bJ27VotWrRIe/fuVaVKlXTPPfeoVatWatu2raNLww0aO3asTp8+rQ8++CDX8osXL2r37t1699139cknn6hatWp68sknNWrUKD7bxdCuXbv09ttva8mSJapbt65CQ0PVq1cvlStXztGlFVoEIkj6v8CzZMkSzZ49Wzt27NADDzygIUOGqGnTpqpUqZKjS8QNys7O1s6dO1WvXj1zUr7Nmzdr6dKlWrdunVq1aqX58+c7uErcqJ07dyo7O1tNmzbV888/rypVqmjgwIHy8PCQ9Oep0f3792vBggV68803tWvXLjVq1MjBVeNG5VwU8/HHHyswMFC33XabpD+HPEyfPl2LFy+Wj4+P/vOf/2jcuHFycXFxcMWFkAFcxddff23cddddhs1mMzp37mwsXbrUOHPmjKPLwnW6cuWKYRiGERUVZTz44IOGr6+vYbPZjGHDhhnp6elmu23bthmxsbG5XoOiLSsryxg5cqTh4uJi1KxZ05g+fbpx6tQpc/2VK1eMEydOGIZhGNnZ2Y4qEwUoOzvbqFu3rrFx40bDMP78P5Dj5MmTxsiRI42mTZs6qrxCjwEDFmf8pYMwPT1dBw8eVEZGhu6//37t3LlTW7ZskWEYCg4O1u+//+7ASpEfOXMIjRgxQjVr1tS+ffvUsGFDXbx4UaVKldKxY8eUnp6uZs2amb0EzDtUtGVlZUmSPvvsMz399NP6/fff9fDDD2vixIkKCAjQiy++qGPHjqlkyZLmFUacMivasrOzdfnyZdlsNgUFBenjjz+W9H/H1TAMValSRTNmzNCWLVscWWqhRiCyMOP/nybLzMzU1KlTFRAQoP79+5td7adPn1bz5s317bff6ujRo2rSpImjS8Z1yBkoP3v2bLm7u2vatGk6ffq0jhw5otDQUEnSl19+qSeeeELx8fEOrBQFqWTJkrp48aKeeuopJSYmqmrVqpo8ebIOHz6s0aNHa8mSJapRo4YiIiIcXSpu0NGjR3X06FGVKFHCnE+obdu22r59u86fP28GoosXL2rjxo2KiIhgeo1/QCCysJy/JF966SV99NFHevTRRzV06FD16NFD0dHRevTRR3XkyBFJf97vyGC4WZGSc1XJ2bNnFRQUJEl6+eWX1bZtWzVo0ECSVKlSJR09elRVqlRxWJ0oODmf0U2bNqlly5Zq0qSJsrOzlZ2drXLlyum5557T/v37NWvWLN11112SlOsedihapk2bJn9/f/Xv319xcXGSpL59++rMmTPaunWrjhw5orCwMN1999167LHH5Orq6uCKCzlHnq9D4VC+fHlj+fLl5vOUlBRj9erVRu3atY1Ro0Y5sDLk11/HDqxcudLw8fExNm3aZHh4eBi7du0y191zzz3G888/bxgGY4eKk7feesto1aqV8fPPP+da/tf/Fyj6Tp06ZSxbtsy45557DJvNZrRt29aIi4sznn32WcPPz8+oUaOGcc899xjz5s0zjh075uhyCz0CkcXt3bvXaNiwobFly5Y866ZPn260bt3aOH36tAMqQ0EIDw83zpw5Yzz11FOGv7+/Ubt2bSM2NtY4efKkMWHCBMPLy8s4f/68YRgMrC0u1qxZY5QoUcKw2WzGAw88YKxfv95IS0tzdFm4iVJSUoyvv/7a6Ny5s2Gz2QybzWa0adPG+P3333O14zP+z7js3uIuXLigdu3aydvbW/PmzVPlypXNQbUff/yxnnvuOR07dszBVeJa5MxEu2fPHtWrV08rV65Uz549lZmZqV27dmnu3LnauXOnLl++rN9++02tW7fWY489pv79+3MT12Lm2LFjioyMNMePde/eXZ07d1ajRo24PUcxZRiG0tPT9eOPPyoyMlKLFi3S7bffrqefflpDhw51dHlFAoEIio6O1ogRI3T33Xfr0UcfVdWqVXXq1CmNHz9eHTt21BtvvKErV64wGK+IaNeunapXr66NGzeqd+/emjp1qrlu3bp1Onv2rCpVqqSgoCBzhmKDiTeLtJwwfP78ef3xxx86efKkfHx85O/vrw8++ECzZs2Sk5OTAgMD9fLLL6tWrVqOLhk3KOczaxiGLl68KDc3N3PZ5cuXtXv3br3//vt67733NHbsWAbRXwMCkYX99ttvcnd3l5eXl7766iu9+uqrOnbsmLy8vHTkyBH16dNHc+bMkaurK1+YRYBhGMrIyNCCBQu0ePFibd++XT179lSHDh3Upk0b85YrkvTWW2+padOm5mBrFA9DhgzRV199JQ8PDyUkJMjPz08zZsxQx44d9dZbb2nRokXasmWLGYRRdOWE4A8++ECLFy9WfHy8evbsqfvvv1+NGzc2Z6Tev3+/qlSpwgzV14BAZDE5p0bWrFmjadOmafTo0erYsaO5fvv27UpOTpafn5/8/PxUpkyZPDcFROH38ssv69ChQ7p06ZIOHTqkatWqqVWrVmrTpo08PT3l7+/PDMXFRM7nc/ny5Ro4cKCWLFkiPz8/nT9/XsuWLdOnn36q8ePHa+TIkXleg6Ip5/jt2rVLzZs31zPPPKNSpUopMjJSFy5cUNeuXdW3b1/deeed8vLycnS5RQaByGJyenruvPNOderUSRMmTJC7u7syMzPNqdw5PVb0/fULb8WKFVq2bJkOHTqk7OxsJScn66677tKnn37KF2MxMmHCBGVlZZmnRnJOpUyfPl1z587V+vXrVbduXQdXiYI0atQoXbp0SXPmzDGXLV26VNOmTdOxY8d0991365NPPjFv24J/xreeheSEoZ9//lknT57UqFGj5O7uLklmGNq3b59+++03tWvXTmXLlnVkubgBfw053bt3V/fu3bVp0yZt2rRJderUUYsWLSSJuaWKuJxAGx8fr9OnT+v8+fPmOpvNJjc3Nz3zzDOKjIzUjz/+SCAqBnKOeUZGhu644w6dOXMm1/oBAwZowIABWr58ub7//nvC0HUgEFlIzhig8+fPq0KFCoqPj5eXl5euXLmikiVLymaz6dSpUwoPD1dQUBCBqJhp2bKlWrZsmWsZV5YVbTnB95133tGnn36qCxcu6I477tCAAQNUo0YNSdLp06d14MABc6Z5xgMWbTnH/LnnntPs2bNVtWpVtWvXTo0bN8418eIDDzygBx54wEFVFk2cMrOg1NRU1alTR/369dOMGTNyrRs5cqTi4uK0Zs0aTqcARURWVpa2b9+u119/Xb///rsaNGigmjVrqlSpUjpw4IB8fHw0Y8YMXb582bzFA4q2s2fPaubMmZo+fbrKlSunZ555Rg8++KB53HH9CEQWkzOoeunSpRo3bpwCAwPVtWtXtW7dWh9++KEWL16sqKgotWjRgrlpgCLGMAzFxsZqypQpWrt2rVJSUjR48GAFBwebp0lRdOX8Tv7888+VkZGh3r17Kzs7W1OnTtXUqVPl7OysJ554Qg899JCaNm3KH7TXiUBkQTmDpj/77DN9+umniouL08GDB9WkSRM99thjGjp0KN3qQBF35MgRTZkyRbt27VL58uXVokULderUSXfffbejS8MNuv/++xUXF6fAwED17NlTvXv3VpkyZTRv3jyNHj1a/v7+2r17t6PLLHIIRBaQc+rr6NGj+vbbb/X777/LZrNp7Nixunz5suLj41WhQgWVLl1aFStWlMQ4A6C4OHnypObOnatFixYpLCxMI0aMcHRJKABRUVGaP3++Dh48qBo1aujBBx/UI488ogoVKujo0aPy8/NzdIlFDoGomMsJNhcvXtR9992nM2fOqH379nrrrbf06quvasKECcrIyOAuyEAxd/bsWTk5OXHVUTGzZs0azZgxQ+vXr1dgYKD69u2r4cOHO7qsIokTjMVcdna2JCk8PFyZmZnas2ePgoODVb58eQ0YMECStGDBAnMQNYDiqUKFCoShIiorK0uSlJiYqOTk5FzrOnbsqFWrVqlNmzY6ffq0ypcv74gSiwUuuy/mcgZFb9iwQWFhYZKkiRMnqnv37qpRo4YyMjJ09OhRHTlyJNeM1QCAwiHn9/iQIUNUtWpVPfjgg2rcuLEqV65stnnwwQfl4uKi4OBgR5VZ5NFDZAEZGRmqXbu2fv31V504cULr1q3T6NGjJUnOzs6Kjo6Wv7+/JNFLBACF1IgRI7R7924988wzmjBhgpYvX66jR4/qwoULioqKYiD1DWIMkUV88MEH+uqrr2QYhpydnfX5559Lkj766COFhIQoNTXVwRUCAK7F7t27NW7cOB08eFDVqlXTkSNHZLPZtGPHDvPCGFw/AlExlTNfxYEDB+Ts7Cx3d3c9/vjjWrlype6++24NHDhQy5cvV0pKih566CGNGTOGe5gBQBFy/PhxffrppypdurSaNm1qzkaO/CEQFXP33nuv7rjjDs2aNUtubm5av369wsPDlZaWptq1a2vw4MHq0KGDSpYsyaX2AADLojugGMrpHfryyy91+PBhRUZGqlSpUjp37py++uortW3bVvXr11efPn1yvY4wBACwKnqIirGOHTuqUaNGmjZtmnbu3KmZM2dq06ZNqlChgs6dO6cNGzaoWrVqji4TAACH4yqzYiozM1N+fn7m/BUTJkyQk5OTvvvuO/3000+qXLmy9uzZ4+AqAQAoHAhExZSLi4vatGmjN998U9WqVdOJEyc0btw4Va9eXceOHdPevXt12223SfpzNmsAAKyMU2bF3J49e7Rnzx41b95cNWvWVGJiol5++WX9+uuvWrdunXmfMwAArIxB1cVcQECAAgICJP3ZExQZGaktW7boww8/NJcBAGB19BBZSFZWln777TedPXtWQUFBji4HAIBCg0AEAAAsj8EjAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAIqcjRs3ymazKSUlxdGlqEaNGpo1a5ajywBwgwhEAHANFi5cqHLlyuVZvmPHDg0dOvTWFwSgQHHrDgC4AZUrV3Z0CQAKAD1EABzOMAxNmTJFNWvWVOnSpdWoUSN99tln5vqVK1fq9ttvV+nSpdW2bVsdOXIk1+vDw8PVuHHjXMtmzZqlGjVq5Fr24Ycfqn79+nJ1dVWVKlX07LPPmutmzJihgIAAubm5ydfXVyEhITp//rykP0/RPf7440pNTZXNZpPNZlN4eLikvKfMjh07pp49e6ps2bLy8PBQnz59dOrUqTy1LlmyRDVq1JDdblffvn117ty5/P8AAdwwAhEAh5swYYIWLFiguXPnau/evRo5cqQGDBigmJgYHT9+XL169dJ9992n2NhYDR48WOPGjbvubcydO1fPPPOMhg4dqj179uirr77SbbfdZq4vUaKE3nrrLcXFxWnRokVav369xowZI0lq0aKFZs2aJQ8PD8XHxys+Pl6jR4/Osw3DMPTAAw/o7NmziomJ0Zo1a3To0CE98sgjudodOnRIy5cv19dff62vv/5aMTExeuONN657nwAUHE6ZAXCoCxcuaMaMGVq/fr150+GaNWtq06ZNmjdvnmrUqKGaNWtq5syZstlsqlOnjvbs2aPJkydf13Zee+01hYWFacSIEeaypk2bmv8ODQ01/+3v76+JEyfq6aef1pw5c+Ti4iK73S6bzSZvb+//uY21a9dq9+7dOnz4sHx9fSVJS5YsUf369bVjxw5ze9nZ2Vq4cKHc3d0lScHBwVq3bp1ef/3169onAAWHQATAofbt26dLly6pY8eOuZZnZmbqzjvvVHp6upo3by6bzWauywlO1yoxMVEnT55U+/bt/2ebDRs2aNKkSdq3b5/S0tJ05coVXbp0SRcuXJCbm9s1bWf//v3y9fU1w5Ak1atXT+XKldP+/fvNQFSjRg0zDElSlSpVlJiYeF37BKBgEYgAOFR2drYk6ZtvvlHVqlVzrXN1ddWwYcP+9T1KlCghwzByLbt8+bL579KlS//j648ePar77rtPTz31lCZOnKgKFSpo06ZNGjRoUK73+TeGYeQKbv9rubOzc671NpvN/DkAcAwCEQCHqlevnlxdXXXs2DG1bt36quuXL1+ea9m2bdtyPa9cubISEhJyBY/Y2Fhzvbu7u2rUqKF169apbdu2ebbx448/6sqVK5o+fbpKlPhzaOUnn3ySq42Li4uysrL+dV+OHTum48ePm71E+/btU2pqqurWrfuPrwXgWAQiAA7l7u6u0aNHa+TIkcrOzlbLli2VlpamLVu2qGzZsnrqqac0ffp0jRo1Sk8++aR27typhQsX5nqPNm3aKCkpSVOmTNHDDz+s6OhorVq1Sh4eHmab8PBwPfXUU/L09FTXrl117tw5bd68WcOGDVOtWrV05coVvf322+revbs2b96sd999N9c2atSoofPnz2vdunVq1KiRypQpozJlyuRq06FDBzVs2FD9+/fXrFmzdOXKFYWEhKh169Zq0qTJTfsZArhxXGUGwOEmTpyol156SREREapbt646d+6sFStWyN/fX9WrV9fnn3+uFStWqFGjRnr33Xc1adKkXK+vW7eu5syZo3feeUeNGjXSDz/8kOcqsIEDB2rWrFmaM2eO6tevr27duungwYOSpMaNG2vGjBmaPHmyGjRooI8++kgRERG5Xt+iRQs99dRTeuSRR1S5cmVNmTIlz37YbDYtX75c5cuXV6tWrdShQwfVrFlTH3/8cQH/xAAUNJvx9xPvAAAAFkMPEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsLz/B6RghWwVzyRfAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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Hx0dNmjTRxIkTFRISot69e0s6M+rUr18/jRw5UosWLZIkjRo1SlFRUed1hhkAAKjbTA1EXbt2VUpKiqZMmaIZM2YoKChIc+fO1X333WfUTJo0SSUlJRozZowKCgoUFhamdevWycvLy6hJTEyUq6urhgwZopKSEkVGRmrp0qVycXExalauXKlx48YZZ6NFR0crKSnpyu0sAACotUy9DtHVhOsQoa7iOkT4Na5DhLrmqrgOEQAAQG1AIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZnaiCaNm2abDZbtYe/v7+x3ul0atq0aQoICJC7u7siIiK0Z8+eatsoLS3V2LFj1bRpU3l6eio6OlqHDx+uVlNQUKDY2FjZ7XbZ7XbFxsaqsLDwSuwiAAC4Cpg+QtS+fXvl5OQYj127dhnrZs2apTlz5igpKUnbt2+Xv7+/+vTpo+PHjxs1cXFxSklJUXJystLT01VcXKyoqChVVFQYNTExMcrKylJqaqpSU1OVlZWl2NjYK7qfAACg9nI1vQFX12qjQlWcTqfmzp2rqVOnatCgQZKkZcuWyc/PT2+++aZGjx4th8OhV199VcuXL1fv3r0lSStWrFBgYKA2bNig22+/XdnZ2UpNTVVmZqbCwsIkSUuWLFF4eLi+/vprtWnT5srtLAAAqJVMHyH69ttvFRAQoKCgIN1zzz364YcfJEn79+9Xbm6u+vbta9S6ubmpZ8+eysjIkCTt2LFD5eXl1WoCAgLUoUMHo2bLli2y2+1GGJKkbt26yW63GzUAAMDaTB0hCgsL0xtvvKEbb7xRR48e1bPPPqvu3btrz549ys3NlST5+flVe42fn58OHDggScrNzVWDBg3UuHHjGjVVr8/NzZWvr2+N9/b19TVqzqW0tFSlpaXG86KiogvbSQAAUOuZGoj69+9v/DskJETh4eG6/vrrtWzZMnXr1k2SZLPZqr3G6XTWWHa2s2vOVf9720lISND06dPPaz8AAMDVzfRDZr/m6empkJAQffvtt8a8orNHcfLy8oxRI39/f5WVlamgoOA3a44ePVrjvfLz82uMPv3alClT5HA4jMehQ4cuat8AAEDtVasCUWlpqbKzs9W8eXMFBQXJ399f69evN9aXlZUpLS1N3bt3lySFhoaqfv361WpycnK0e/duoyY8PFwOh0Pbtm0zarZu3SqHw2HUnIubm5u8vb2rPQAAQN1k6iGziRMn6q677lLLli2Vl5enZ599VkVFRRo2bJhsNpvi4uIUHx+v4OBgBQcHKz4+Xh4eHoqJiZEk2e12jRgxQhMmTJCPj4+aNGmiiRMnKiQkxDjrrG3bturXr59GjhypRYsWSZJGjRqlqKgozjADAACSTA5Ehw8f1r333qtjx46pWbNm6tatmzIzM9WqVStJ0qRJk1RSUqIxY8aooKBAYWFhWrdunby8vIxtJCYmytXVVUOGDFFJSYkiIyO1dOlSubi4GDUrV67UuHHjjLPRoqOjlZSUdGV3FgAA1Fo2p9PpNLuJq0FRUZHsdrscDgeHz1CnHJwRYnYLqEVaPr3r94uAq8j5fn/XqjlEAAAAZiAQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy7ugQNSrVy8VFhbWWF5UVKRevXpdbE8AAABX1AUFok2bNqmsrKzG8lOnTumzzz676KYAAACupD90t/uvvvrK+PfevXuVm5trPK+oqFBqaqquvfbaS9cdAADAFfCHAlGnTp1ks9lks9nOeWjM3d1d8+bNu2TNAQAAXAl/KBDt379fTqdTrVu31rZt29SsWTNjXYMGDeTr6ysXF5dL3iQAAMDl9IcCUatWrSRJlZWVl6UZAAAAM/yhQPRr33zzjTZt2qS8vLwaAenpp5++6MYAAACulAsKREuWLNHf//53NW3aVP7+/rLZbMY6m81GIAIAAFeVCwpEzz77rJ577jlNnjz5UvcDAABwxV3QdYgKCgo0ePDgS90LAACAKS4oEA0ePFjr1q271L0AAACY4oIOmd1www166qmnlJmZqZCQENWvX7/a+nHjxl2S5gAAAK4Em9PpdP7RFwUFBf33Ddps+uGHHy6qqdqoqKhIdrtdDodD3t7eZrcDXDIHZ4SY3QJqkZZP7zK7BeCSOt/v7wsaIdq/f/8FNwYAAFDbXNAcIgAAgLrkgkaIhg8f/pvrX3vttQtqBgAAwAwXFIgKCgqqPS8vL9fu3btVWFh4zpu+AgAA1GYXFIhSUlJqLKusrNSYMWPUunXri24KAADgSrpkc4jq1aunxx9/XImJiZdqkwAAAFfEJZ1U/f333+v06dOXcpMAAACX3QUdMhs/fny1506nUzk5Ofroo480bNiwS9IYAADAlXJBgWjnzp3VnterV0/NmjXTiy+++LtnoAEAANQ2FxSINm7ceKn7AAAAMM1FzSHKz89Xenq6Nm/erPz8/ItqJCEhQTabTXFxccYyp9OpadOmKSAgQO7u7oqIiNCePXuqva60tFRjx45V06ZN5enpqejoaB0+fLhaTUFBgWJjY2W322W32xUbG6vCwsKL6hcAANQdFxSITpw4oeHDh6t58+bq0aOHbr31VgUEBGjEiBE6efLkH97e9u3btXjxYt18883Vls+aNUtz5sxRUlKStm/fLn9/f/Xp00fHjx83auLi4pSSkqLk5GSlp6eruLhYUVFRqqioMGpiYmKUlZWl1NRUpaamKisrS7GxsRey6wAAoA66oEA0fvx4paWl6cMPP1RhYaEKCwv1/vvvKy0tTRMmTPhD2youLtZ9992nJUuWqHHjxsZyp9OpuXPnaurUqRo0aJA6dOigZcuW6eTJk3rzzTclSQ6HQ6+++qpefPFF9e7dW507d9aKFSu0a9cubdiwQZKUnZ2t1NRUvfLKKwoPD1d4eLiWLFmi1atX6+uvv76Q3QcAAHXMBQWiVatW6dVXX1X//v3l7e0tb29v3XHHHVqyZIn+/e9//6FtPfLII7rzzjvVu3fvasv379+v3Nxc9e3b11jm5uamnj17KiMjQ5K0Y8cOlZeXV6sJCAhQhw4djJotW7bIbrcrLCzMqOnWrZvsdrtRAwAArO2CJlWfPHlSfn5+NZb7+vr+oUNmycnJ+uKLL7R9+/Ya63JzcyWpxvv4+fnpwIEDRk2DBg2qjSxV1VS9Pjc3V76+vufstarmXEpLS1VaWmo8LyoqOs+9AgAAV5sLGiEKDw/XM888o1OnThnLSkpKNH36dIWHh5/XNg4dOqTHHntMK1asUMOGDf9rnc1mq/bc6XTWWHa2s2vOVf9720lISDAmYdvtdgUGBv7mewIAgKvXBQWiuXPnKiMjQy1atFBkZKR69+6twMBAbd68WS+99NJ5bWPHjh3Ky8tTaGioXF1d5erqqrS0NL388stydXU1RobOHsXJy8sz1vn7+6usrKzGzWbPrjl69GiN98/Pzz/nKFeVKVOmyOFwGI9Dhw6d134BAICrzwUFopCQEH377bdKSEhQp06ddPPNN+v555/Xd999p/bt25/XNiIjI7Vr1y5lZWUZjy5duui+++5TVlaWWrduLX9/f61fv954TVlZmdLS0tS9e3dJUmhoqOrXr1+tJicnR7t37zZqwsPD5XA4tG3bNqNm69atcjgcRs25uLm5GfOjqh4AAKBuuqA5RAkJCfLz89PIkSOrLX/ttdeUn5+vyZMn/+42vLy81KFDh2rLPD095ePjYyyPi4tTfHy8goODFRwcrPj4eHl4eCgmJkaSZLfbNWLECE2YMEE+Pj5q0qSJJk6cqJCQEGOSdtu2bdWvXz+NHDlSixYtkiSNGjVKUVFRatOmzYXsPgAAqGMuaIRo0aJFuummm2osb9++vRYuXHjRTVWZNGmS4uLiNGbMGHXp0kU//fST1q1bJy8vL6MmMTFRd999t4YMGaJbbrlFHh4e+vDDD+Xi4mLUrFy5UiEhIerbt6/69u2rm2++WcuXL79kfQIAgKubzel0Ov/oixo2bKjs7GwFBQVVW/7DDz+oXbt21SZb1xVFRUWy2+1yOBwcPkOdcnBGiNktoBZp+fQus1sALqnz/f6+oBGiqgnUZ9u8ebMCAgIuZJMAAACmuaA5RA899JDi4uJUXl6uXr16SZI++eQTTZo06Q9fqRoAAMBsFxSIJk2apF9++UVjxoxRWVmZpDOH0SZPnqwpU6Zc0gYBAAAutwsKRDabTTNnztRTTz2l7Oxsubu7Kzg4WG5ubpe6PwAAgMvuggJRlWuuuUZdu3a9VL0AAACY4oImVQMAANQlBCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5pgaiBQsW6Oabb5a3t7e8vb0VHh6ujz/+2FjvdDo1bdo0BQQEyN3dXREREdqzZ0+1bZSWlmrs2LFq2rSpPD09FR0drcOHD1erKSgoUGxsrOx2u+x2u2JjY1VYWHgldhEAAFwFTA1ELVq00PPPP6/PP/9cn3/+uXr16qUBAwYYoWfWrFmaM2eOkpKStH37dvn7+6tPnz46fvy4sY24uDilpKQoOTlZ6enpKi4uVlRUlCoqKoyamJgYZWVlKTU1VampqcrKylJsbOwV318AAFA72ZxOp9PsJn6tSZMmmj17toYPH66AgADFxcVp8uTJks6MBvn5+WnmzJkaPXq0HA6HmjVrpuXLl2vo0KGSpCNHjigwMFBr1qzR7bffruzsbLVr106ZmZkKCwuTJGVmZio8PFz79u1TmzZtzquvoqIi2e12ORwOeXt7X56dB0xwcEaI2S2gFmn59C6zWwAuqfP9/q41c4gqKiqUnJysEydOKDw8XPv371dubq769u1r1Li5ualnz57KyMiQJO3YsUPl5eXVagICAtShQwejZsuWLbLb7UYYkqRu3brJbrcbNedSWlqqoqKiag8AAFA3mR6Idu3apWuuuUZubm56+OGHlZKSonbt2ik3N1eS5OfnV63ez8/PWJebm6sGDRqocePGv1nj6+tb4319fX2NmnNJSEgw5hzZ7XYFBgZe1H4CAIDay/RA1KZNG2VlZSkzM1N///vfNWzYMO3du9dYb7PZqtU7nc4ay852ds256n9vO1OmTJHD4TAehw4dOt9dAgAAVxnTA1GDBg10ww03qEuXLkpISFDHjh310ksvyd/fX5JqjOLk5eUZo0b+/v4qKytTQUHBb9YcPXq0xvvm5+fXGH36NTc3N+Pst6oHAACom0wPRGdzOp0qLS1VUFCQ/P39tX79emNdWVmZ0tLS1L17d0lSaGio6tevX60mJydHu3fvNmrCw8PlcDi0bds2o2br1q1yOBxGDQAAsDZXM9/8ySefVP/+/RUYGKjjx48rOTlZmzZtUmpqqmw2m+Li4hQfH6/g4GAFBwcrPj5eHh4eiomJkSTZ7XaNGDFCEyZMkI+Pj5o0aaKJEycqJCREvXv3liS1bdtW/fr108iRI7Vo0SJJ0qhRoxQVFXXeZ5gBAIC6zdRAdPToUcXGxionJ0d2u10333yzUlNT1adPH0nSpEmTVFJSojFjxqigoEBhYWFat26dvLy8jG0kJibK1dVVQ4YMUUlJiSIjI7V06VK5uLgYNStXrtS4ceOMs9Gio6OVlJR0ZXcWAADUWrXuOkS1FdchQl3FdYjwa1yHCHXNVXcdIgAAALMQiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOW5mt2AlYT+4w2zW0Ats2P2A2a3AAAQI0QAAAAEIgAAAAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPFMDUUJCgrp27SovLy/5+vrq7rvv1tdff12txul0atq0aQoICJC7u7siIiK0Z8+eajWlpaUaO3asmjZtKk9PT0VHR+vw4cPVagoKChQbGyu73S673a7Y2FgVFhZe7l0EAABXAVMDUVpamh555BFlZmZq/fr1On36tPr27asTJ04YNbNmzdKcOXOUlJSk7du3y9/fX3369NHx48eNmri4OKWkpCg5OVnp6ekqLi5WVFSUKioqjJqYmBhlZWUpNTVVqampysrKUmxs7BXdXwAAUDvZnE6n0+wmquTn58vX11dpaWnq0aOHnE6nAgICFBcXp8mTJ0s6Mxrk5+enmTNnavTo0XI4HGrWrJmWL1+uoUOHSpKOHDmiwMBArVmzRrfffruys7PVrl07ZWZmKiwsTJKUmZmp8PBw7du3T23atPnd3oqKimS32+VwOOTt7X1B+8fNXXG22nBz14MzQsxuAbVIy6d3md0CcEmd7/d3rZpD5HA4JElNmjSRJO3fv1+5ubnq27evUePm5qaePXsqIyNDkrRjxw6Vl5dXqwkICFCHDh2Mmi1btshutxthSJK6desmu91u1JyttLRURUVF1R4AAKBuqjWByOl0avz48frzn/+sDh06SJJyc3MlSX5+ftVq/fz8jHW5ublq0KCBGjdu/Js1vr6+Nd7T19fXqDlbQkKCMd/IbrcrMDDw4nYQAADUWrUmED366KP66quv9K9//avGOpvNVu250+mssexsZ9ecq/63tjNlyhQ5HA7jcejQofPZDQAAcBWqFYFo7Nix+uCDD7Rx40a1aNHCWO7v7y9JNUZx8vLyjFEjf39/lZWVqaCg4Ddrjh49WuN98/Pza4w+VXFzc5O3t3e1BwAAqJtMDUROp1OPPvqo3n33Xf3nP/9RUFBQtfVBQUHy9/fX+vXrjWVlZWVKS0tT9+7dJUmhoaGqX79+tZqcnBzt3r3bqAkPD5fD4dC2bduMmq1bt8rhcBg1AADAulzNfPNHHnlEb775pt5//315eXkZI0F2u13u7u6y2WyKi4tTfHy8goODFRwcrPj4eHl4eCgmJsaoHTFihCZMmCAfHx81adJEEydOVEhIiHr37i1Jatu2rfr166eRI0dq0aJFkqRRo0YpKirqvM4wAwAAdZupgWjBggWSpIiIiGrLX3/9dT344IOSpEmTJqmkpERjxoxRQUGBwsLCtG7dOnl5eRn1iYmJcnV11ZAhQ1RSUqLIyEgtXbpULi4uRs3KlSs1btw442y06OhoJSUlXd4dBAAAV4VadR2i2ozrEOFy4DpEqG24DhHqmqvyOkQAAABmIBABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLMzUQffrpp7rrrrsUEBAgm82m9957r9p6p9OpadOmKSAgQO7u7oqIiNCePXuq1ZSWlmrs2LFq2rSpPD09FR0drcOHD1erKSgoUGxsrOx2u+x2u2JjY1VYWHiZ9w4AAFwtTA1EJ06cUMeOHZWUlHTO9bNmzdKcOXOUlJSk7du3y9/fX3369NHx48eNmri4OKWkpCg5OVnp6ekqLi5WVFSUKioqjJqYmBhlZWUpNTVVqampysrKUmxs7GXfPwAAcHVwNfPN+/fvr/79+59zndPp1Ny5czV16lQNGjRIkrRs2TL5+fnpzTff1OjRo+VwOPTqq69q+fLl6t27tyRpxYoVCgwM1IYNG3T77bcrOztbqampyszMVFhYmCRpyZIlCg8P19dff602bdpcmZ0FAAC1Vq2dQ7R//37l5uaqb9++xjI3Nzf17NlTGRkZkqQdO3aovLy8Wk1AQIA6dOhg1GzZskV2u90IQ5LUrVs32e12owYAAFibqSNEvyU3N1eS5OfnV225n5+fDhw4YNQ0aNBAjRs3rlFT9frc3Fz5+vrW2L6vr69Rcy6lpaUqLS01nhcVFV3YjgAAgFqv1o4QVbHZbNWeO53OGsvOdnbNuep/bzsJCQnGJGy73a7AwMA/2DkAALha1NpA5O/vL0k1RnHy8vKMUSN/f3+VlZWpoKDgN2uOHj1aY/v5+fk1Rp9+bcqUKXI4HMbj0KFDF7U/AACg9qq1gSgoKEj+/v5av369saysrExpaWnq3r27JCk0NFT169evVpOTk6Pdu3cbNeHh4XI4HNq2bZtRs3XrVjkcDqPmXNzc3OTt7V3tAQAA6iZT5xAVFxfru+++M57v379fWVlZatKkiVq2bKm4uDjFx8crODhYwcHBio+Pl4eHh2JiYiRJdrtdI0aM0IQJE+Tj46MmTZpo4sSJCgkJMc46a9u2rfr166eRI0dq0aJFkqRRo0YpKiqKM8wAAIAkkwPR559/rttuu814Pn78eEnSsGHDtHTpUk2aNEklJSUaM2aMCgoKFBYWpnXr1snLy8t4TWJiolxdXTVkyBCVlJQoMjJSS5culYuLi1GzcuVKjRs3zjgbLTo6+r9e+wgAAFiPzel0Os1u4mpQVFQku90uh8NxwYfPQv/xxiXuCle7HbMfMLsFHZwRYnYLqEVaPr3L7BaAS+p8v79r7RwiAACAK4VABAAALI9ABAAALI9ABAAALK/W3roDAGBNt8y7xewWUItsHrv5irwPI0QAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyLBWI/vnPfyooKEgNGzZUaGioPvvsM7NbAgAAtYBlAtFbb72luLg4TZ06VTt37tStt96q/v376+DBg2a3BgAATGaZQDRnzhyNGDFCDz30kNq2bau5c+cqMDBQCxYsMLs1AABgMksEorKyMu3YsUN9+/attrxv377KyMgwqSsAAFBbuJrdwJVw7NgxVVRUyM/Pr9pyPz8/5ebmnvM1paWlKi0tNZ47HA5JUlFR0QX3UVFacsGvRd10MZ+nS+X4qQqzW0AtUhs+k6dLTpvdAmqRi/1MVr3e6XT+Zp0lAlEVm81W7bnT6ayxrEpCQoKmT59eY3lgYOBl6Q3WZJ/3sNktANUl2M3uAKjGPvnSfCaPHz8uu/2/b8sSgahp06ZycXGpMRqUl5dXY9SoypQpUzR+/HjjeWVlpX755Rf5+Pj81xCF31dUVKTAwEAdOnRI3t7eZrcDSOJzidqHz+Sl43Q6dfz4cQUEBPxmnSUCUYMGDRQaGqr169dr4MCBxvL169drwIAB53yNm5ub3Nzcqi1r1KjR5WzTUry9vfklR63D5xK1DZ/JS+O3RoaqWCIQSdL48eMVGxurLl26KDw8XIsXL9bBgwf18MMcsgAAwOosE4iGDh2qn3/+WTNmzFBOTo46dOigNWvWqFWrVma3BgAATGaZQCRJY8aM0ZgxY8xuw9Lc3Nz0zDPP1DgcCZiJzyVqGz6TV57N+XvnoQEAANRxlrgwIwAAwG8hEAEAAMsjEAEAAMsjEAHA76isrDS7BQCXGYEIAH5HvXr8V4naq+rcKIL7xeG3HADOUlFx5oa3S5cu1V133aWTJ0+a3BHw31XdTorgfnEsdR0iXJ0qKytls9m4hxyuGBcXF0nSqlWrFB4eLg8PD5M7Aqqrujl5VlaW1q5dq6ysLAUFBemJJ54wbvXxWzcwR03ESdQaVcO+FRUVcjgc2rx5s/Lz81WvXj3ZbDY5nU5x2SxcblWHHXJyctSmTRvjBtCVlZUckkCtUFFRIZvNpr1792rkyJFKSUnRDTfcoOeff16rV6+WJJWXlxOG/iACEWqNqi+bF154Qd27d9fw4cMVERGh0aNHKzs7m1EiXBFVhx3mzZunN998U8nJyTp16pTq1aunevXqyel0Eoxgqqr/BydNmqR27dopMzNT7du31/XXX68BAwbo9OnTevbZZ7V582aTO726EIhQa7i4uOjYsWOaNm2aRo0apaeffloPPPCA9u7dqz59+ui+++5Tenq62W3CAsrLy9WoUSP9z//8j3bu3KnevXtr3rx5OnLkiGw2G3M1YKp69eqpsLBQ3333nXE7queee07Dhw+Xp6enSktL9dNPPykzM9PkTq8u3LoDtULVse4NGzZoyZIleuuttyRJpaWl2rdvnz777DN99NFH+s9//qP09HR17drV5I5hBUePHtXHH3+sdevWaf/+/fL29tZtt92mgQMHqk2bNma3BwsrKSnRXXfdpQceeEBBQUH6y1/+ouzsbDVu3FiFhYXq2LGjli9froiICFVWVhLizwOTqlEr2Gw2VVRUaPPmzSouLlZJSYnc3d3l5uamjh07KiQkRJGRkcrOziYM4YqoqKiQn5+fHnzwQUVHRys1NVWbNm3SG2+8oYKCAs2cOdPsFmFh7u7u6t27t1JTU7Vnzx7FxMTIx8dHFRUVeumll+Tp6amIiAhJnH12vhghQq2xdu1a/e1vf9Px48cVFxenCRMmqFGjRma3BYuo+iv622+/1dq1a/Xzzz/L09NTQ4cOVWBgoKQzf5WvXr1aoaGhat26tckdw6qqRtR/+OEHjR49Wp988on69++vTp06adOmTSorK9P//u//GvOJXF0Z+zgfBCLUGsXFxUpNTdW6dev0n//8R97e3rrrrrs0evRoBQQEmN0e6rCqL5jCwkLdeeedys/PV5cuXbRhwwb5+Piod+/eGjNmjNq2bWt2q0ANq1at0ty5c2W329WkSRONHz9enTp1Mrutqw6BCKaqqKiQi4uLtm7dqtOnT+uWW25Rfn6+MjMztWnTJqWnp6u0tFSdO3fWnDlz1LhxY7NbRh1U9TkcO3asvvrqK6WlpSktLU1RUVGKjY1VSkqK6tevr44dO2rp0qXy8fExu2VYTNVIz7Zt27RmzRp98sknioyM1MiRI3XttddKko4fPy4vLy+TO716EYhQKwwfPlxffvmlli1bpg4dOkiSHA6HduzYofXr12vnzp1as2YNx8Jx2VRUVKhdu3ZKSkpSnz59FB0drZYtWyopKUmTJ0/W22+/rUGDBunFF180u1VYWOvWrdWsWTPdcMMNysjIUG5uroYMGaInnniCEcyLxLcLaoXp06fruuuuU5cuXTRjxgw5HA7Z7Xb16tVLzzzzjF555RXCEC6rffv2qXXr1vL09NSRI0e0a9cu3XfffZKkQYMGKTw8XA8//LDJXcLK3nrrLTVs2FCffvqpli1bpp07d2rRokXatWuX/vSnPykiIkK5ublmt3nV4hsGpisrK1NgYKBWrVqlF154QWlpaXr55Zf1888/S5IaNmyoFi1amNwl6prS0tJqz9u3b68lS5YoODhYJ06ckN1uV3FxsSTp8OHD2rFjh4KDg81oFZAkdejQQQMHDlS9evXk6uqqRo0a6YEHHtDmzZv1r3/9S25ubmrSpInZbV61OGQGU1TN2fjyyy+VnJwsX19f9erVS9dee61efvllLV26VP369VN8fLyaNm1qdruogwYPHqx69epp6tSpCgkJqXYV9NLSUkVFRenEiRO67rrrtG3bNsXGxuqZZ54xsWNY2dq1axUdHS0vLy99+umnateunaTq9yurOlOS6w5dGH5iMEXVzTNfeeUVzZw5UytWrFDv3r0VFRUlm82mwMBAvfLKK+rZsyd3Gsdlcccdd2jfvn3q0qWLBg8erPT0dJ0+fVqS5ObmphkzZqh9+/b65ZdfdO+99+qpp54yuWNYWatWrfS3v/1N7u7uioyMVGJiosrKyoww5HQ6jRBEGLowjBDBVLt379YLL7ygsrIyzZ49Wzk5Ofr8889VWVmpd955R9dff71eeeUVs9tEHfbRRx8pISFBW7ZsUa9evTR+/Hj17t1b9evX18mTJ+Xq6qoGDRqY3SagEydO6ODBg3r11Ve1dOlSVVRU6LHHHtNjjz3GGbiXAIEIpvvpp5/0xBNPaPXq1Zo7d67uv/9+YwSp6tAacLmlp6fr+eef18cff6zOnTvr8ccf18CBA+Xh4WF2a4Ck6v8fHjp0SMuXL9fChQt1+PBhHTt2jPlDF4lAhCuq6nh3Xl6ekpKS9Ne//lWBgYHy8vLSxo0btWrVKoWEhOjhhx8mCOGyqfpiyc3NVX5+voKDg9WwYUNJ0q5duzRr1izj2kPvv/++evToYXLHsJqqeUAFBQX64IMPNH/+fPn7++v6669Xnz591LdvX7m6uurAgQP6+uuv1bdvX+YOXSQCEUyRkpKiKVOmaP/+/brpppvUsmVLnThxQsePH9fBgwc1aNAgzZ8/n19uXFaDBg3Szp079eijjyoyMlLBwcHy9PSUJP3444+aNWuWnnzySc5yxBVXFdpjY2O1a9cude7cWZWVlcrLy1NRUZHuvfdePfroo2a3WacQiGCKoqIieXh4yOFwKCUlRceOHVN+fr42btyorKwsde3aVVu3bjW7TdRxBw8e1MyZM5WcnKxmzZppyJAhioqKUps2bWS3281uDxZVNZJ++PBh3XDDDUpPT1eXLl0kSd98840WLVqkpKQkvf322xowYIDJ3dYdBCJcMb8+PVSScnJy1Lx5c+N51V9E33zzjdzd3Y0bagKXm8Ph0OzZs7VgwQK5ublp2LBh6tWrlyIjIxmlhGlWrlyp2bNn69NPP5W3t3e1/0PvuusutW3bVrNmzTK5y7qD33RcEVW/yJWVlVqwYIF69uypqKgohYaG6l//+pek/3cq/o033kgYwmVR9fffr/8OdDqdstvtevbZZ5WTk6MWLVpo5syZev311wlDMFVoaKiOHTumDz/8UFL1z22bNm305ZdfmtVancRvO66IiooKSdLzzz+vf/7znwoKCtKIESPUsWNHjR49Wr169dKBAwckVf+lBy4Vp9NpfA4XLlyot956S3l5edVqGjRooPvvv1+vvPKK5s6da0KXwP8TFBSk2267TWPHjtXChQtVUlKioqIi7dixQ2+//bZiYmIk/b//X3FxOGSGK8rX11cvv/yy7rnnHklnrquxZcsWTZw4UYMHD9bUqVNN7hB1XXl5uSIjI5Wenq4///nPGjVqlCIiItS8eXO5uLgoMTFRGzdu1AcffGB2q4Ak6cknn9SCBQtUWVmpG2+8UUeOHFF4eLj+/e9/m91anUIgwhWzd+9eDRkyRMuWLVNoaGi1dVOnTlVaWppSUlLUrFkzkzpEXZWUlKS2bduqR48eql+/vqQzp9dPnTpVq1ev1s0336y//vWvOnTokN577z0lJiYaf30DV0rVafPl5eXKz8/XkSNHjMnU+fn5WrNmjfbv368+ffqoXbt2aty4Mddqu4QIRLhiioqKFB4errZt22rZsmXG6c2S9OGHH+rRRx/V/v37mbeBSyo7O1t33nmnWrVqZdwR/NZbb1WjRo0knblx64wZM7R582YFBASoR48e3KYDV9zp06fl6uoqSXrwwQeVkZGhhg0bytPTU8OGDdPIkSMJPpcZgQhX1Nq1azVp0iT16NFD/fr1U6dOnZSXl6e///3v6tq1q+bNm2d2i6hDqibzV825SEtLU7169dSpUydFRESoZ8+e8vPzk3Tmhq4nT57kFggw1fz58/XCCy/oH//4h9zc3LRt2zZt3LhR9erVU2xsrB555BEjzOPSIhDhiiotLdUrr7yi1157TeXl5Tp16pR++ukn9e/fX6+99pq8vb3NbhF1SEFBgRo2bCh3d3dJ0g8//KDly5drw4YNKisrU/v27dWjRw/ddtttatWqlcndwooOHz6shIQEjRs3Tm3atNHkyZPVsmVLPfLII5LOHCrbuXOn1q9frw8++ED16tXT7t27GS26DAhEuGyqjoefPn1aP/zwg06dOqWbb75ZknTq1Cl98MEHcnd3l7e3t0JDQ3XNNdeY3DHqmn/84x/68ssvde+992rAgAHGvZ7y8vK0YsUKffTRR3I4HLrpppvUuXNnPfTQQ1yQEVfUe++9pyeffFIuLi4KCwtT48aNZbPZalxfyOFwaPv27XI6nerTpw9zhy4DAhEum6pA9Nhjj+mTTz7R3r17FRgYqAcffFAjRoxQy5YtzW4Rddjp06f10ksvKSMjQz/99JOaNWum3r17a9CgQcZ1rk6cOKHk5GQlJyfr5MmT+vTTT/mSwRV16tQpffHFF0pLS9Nnn32mffv26dSpU3rjjTfUu3dvs9uzFAIRLouquRs7d+7Un//8Z82bN09du3ZVSkqK5s2bp/Lyco0YMUL33XefQkJCjDN/gEstNzdXa9as0YYNG7R//355eXmpZ8+e+utf/6o2bdpIOvN53bNnjzp06GByt7CqiooKZWVl6dNPP9V7772nAwcOqH379ho+fLj+8pe/mN2eJRCIcFnNmzdP+/bt0/z5841lFRUVWrRokWbPnq0DBw7o0KFDuvbaa03sEnXR2Xf+/uWXX7R+/XqtXbtW2dnZcnd3V3h4uAYOHGic2gyY4ezP6g8//KD//Oc/Sk1N1d69e+Xr66uYmBiNGjXKxC7rPgIRLrmq0aGCggKtWrVKGzZs0Pz58+Xj41PjFz8tLU09e/Y0sVtYwa9PaT59+rTWrVun1atXa9++fcrNzdX06dM1ePBgk7uE1fz6/8NffvlF+/btU2BgoJo0aSJPT08dPnxYmzZt0qpVq3TdddcpMTHR5I7rNgIRLpuXX35ZcXFxks6MFD3wwAPy8vKSVPNGr8ClVvUZy8jI0NKlS/XVV1+pc+fOevDBBxUWFian02lcDHTq1Kny9fU1u2VYTNXE6ISEBK1atUr79u1TRUWFevXqVe1Q2eHDh+Xl5SW73V7jj0pcOvxUcdmMGzdOO3bs0IMPPqi4uDjdcssteu2111RYWEgYwmVVUVEhm82mXbt26YEHHtCpU6d09913a9GiRTpy5IikM7fwiIiI0EsvvUQYwhXndDrl4uKiQ4cOacaMGbr77rv1xRdfGDdyvffee/XEE09Iklq0aGGc/UgYunwYIcIlVfUXz1tvvaXQ0FDdcMMNkqRvv/1WL774ot544w0FBATogQce0BNPPKEGDRqY3DHqoqq/ou+66y41b95cixcv1qeffqr7779f+/btU4MGDfR///d/uvXWWzmTB6Zavny5UlJS9O6771ZbnpycrLFjx2rdunXq3LmzSd1ZC1ETl5SLi4ucTqemT5+un376SdKZL6fg4GAtXLhQ33//vaKjo7V69WrCEC6bevXqyeFw6NChQxo4cKAkadSoUXr00Ufl4eEh6UxI37hxo5ltwqKqxiHKyspUXl4u6cwcIunMpSCcTqf69eunwMBAY8QIlx+BCJdMZWWlysvLZbPZFB4errfeekuSjMNjTqdTzZs315w5c5SRkWFmq6jjKisrZbfbdeONN+qnn37Snj17VFhYqL/97W9yOp06fvy4Nm3apD59+pjdKiyo6v/Ed955Rw899JA+/PBDvf/++5IkT09P2Ww2NWrUSHl5ecYoe2VlpWn9WoWr2Q3g6nfgwAFJUqtWrYzj27fddpsSExNVXFxsXIH65MmT2r59u7Zs2aIpU6aY1i/qvqrPYWRkpJ5//nkVFxdrwIABatasmX7++WclJibKbrcrIiLC3EZhabfccouWLVumd999VyNHjlRiYqIef/xxnT59WtnZ2brxxhsVExMjiblDVwJziHDRxo4dq/nz5+vee+/VlClT1KFDB50+fVo33HCDlixZouDgYM2bN0+pqak6ceKExo0bp/Hjx5vdNuqgqjlsH374oXJzczVgwAAtXrxYycnJql+/vgIDA3Xo0CG5urrq//7v/9SvXz+zWwZ04sQJbdq0SW+88YY++ugjnTx5UnfeeaeWL19u3MiVs8suPwIRLlpeXp4++eQTzZ8/XxkZGYqIiNC8efO0cOFCffjhh7LZbLr22mv1wAMPqH///sZtE4BLqeo0+8rKSl133XWaMGGChg8fLnd3d7333nv64osvlJeXp8aNG+vRRx/lZq4wVUFBgbZu3Sqn06lGjRopPDxcx44dU1ZWlj766CNjkvWYMWP02GOPqWHDhiZ3XPcRiHDJOBwOpaena968eVq3bp0kqWfPnnrttdcUFBRk1HENIlwOv76my9tvv63PP/+8xn3JDh48yD30YJqqC4SuWbNG8+fPV3p6uoKDg1VSUqI1a9YYIb2oqEhffvml1qxZo8WLF6tLly5au3atyd3XfYy/4ZKx2+2644479O6772rTpk16+OGHtW3bNg0aNEiLFy826ghDuByqws+OHTsUHR1tPK+srJTT6dTp06f1+uuva8GCBWa2CQurulr6448/ro4dO8rhcOimm25SYGCgWrVqpfz8fKWmpsrDw0O33nqrpk6dqtWrVyspKcnkzq2BQISLUjXA6HQ6deLECdlsNrm7u6tHjx566aWX9OmnnyosLEwPP/wwE6lxWVV9Ftu0aaP3339fxcXFks4EcJvNJldXV6WmphqnOQNXUtXnMy0tTSUlJXr22WdVUVGhtWvXaty4cZKk7777Tq+99pp27twpSbrmmmsUHh6u4OBg0/q2EgIRLkrVL/lrr72mO+64QzfeeKMmTZqkTZs26cSJEwoNDdXChQu1Z88eTZ482eRuUZdVjTxGRkYqJydHM2fO1KlTp2Sz2XT8+HG99dZb2rVrl0aPHm1yp7CaX08TOHHihHx9fVWvXj0lJCSoZcuWxuR+m82m7du36/rrr5fEqfZXGnOIcMGqznrYuXOnunXrpkceeUQNGzZUcnKyTpw4of79++uee+5R586d5efnZ3a7sJDFixdrypQpcnV1Ve/evfX111/r1KlTuu+++xiphKkKCwsVGRmpp59+WuPGjdOCBQt0xx13SJLuuecelZeXa9WqVZxVZgICES7a+PHjderUKf3zn/80lq1YsUIvvPCCDh48qP/5n//R22+/LW9vbxO7hNUcOHBAq1ev1scff6wOHTooKipK3bp1M+ZxAJdbRUWFVq1apVtvvVXNmzc3ls+ePVtPPvmkKioq9PbbbyswMFDLli3TmjVrtHbtWrVp04ZAZAICES5I1S9raWmpli1bpp9//vmcf3m/9957+uyzz/Tiiy+a0CVwRtUhC85wxJX01ltvadKkSYqIiFCPHj3Uq1cv44zbdevWaeHChVq9erUaNWqkrl27asSIERo0aBBhyCQEIlyUcePGKSkpSddee63+/e9/q1OnTnJzczO7LQAwXXFxsXGxxdzcXAUFBalHjx7q27evbrrpJlVUVOj48eP68ccf1b59e9WvX18SlyYxC4EIF+WXX35RYmKiXnzxRTVq1EiPPPKIBg4cqNatW3MhMQDQmesP/fvf/1ZycrIOHDiggIAARUREqE+fPurUqZPZ7eH/RyDCH1Z1AbxVq1aptLRUgwcPVmVlpWbPnq3Zs2erfv36Gj58uP7yl7+oa9euDP0CsCSn06mKiopq89Y+/vhjrVy5Unv27FHTpk11yy23qGfPnrrttttM7BQSgQgX4c4779Tu3bsVGhqqAQMGaPDgwfLw8NCiRYs0ceJEBQUF6auvvjK7TQAwVWVlpXbs2KF27drJ09NTkrR582atWLFCn3zyiXr06KFXXnnF5C5BIMJFSUlJ0SuvvKJvv/1W1113nQYOHKihQ4eqSZMmOnDgAPeLAmBJVSPp7733nt544w19/vnnOnz4sB599FHNmjXLmFKwdetWNWzYUB07djReA3NwLAMXZeDAgfroo480f/58ubi4KC4uTlFRUXr55ZcJQwAsqyrYPPbYY2rdurX27t2rm2++WSdPnlTDhg118OBBlZSUKCwsTB07dqz2GpiDQITzVlFRIenM3e0LCgqqrevTp48+/vhjRURE6NixY2rcuLEZLQKA6aquMJ2UlCQvLy+98MILOnbsmH788UfFxcVJkt5//30NHz5cOTk5JnaKX+MKZThvVX+9jBw5Utdee60GDhyoTp06qVmzZkbNwIED1aBBA8XGxprVJgCYqupEkl9++UXh4eGSpGeeeUa33XabOnToIElq2rSpDhw4UO2CjTAXgQh/2GOPPaann35aGzZs0G233ab+/furc+fOatq0qVJSUtS2bVuzWwQAU/z6oopdu3bVokWLtHnzZr333ntKS0sz6ubPn2+cWcbcodqBSdW4YF999ZWeeOIJffvtt2rRooV+/PFH4+aEPj4+ZrcHAKaZPn26xo4dq6lTp2rt2rVydXXVO++8I19fX/3zn//UkiVL9P3338vT05MLMdYSBCJctEOHDumdd96Ru7u7unbtqi5dupjdEgBcMVWjQrt27VK7du20Zs0aDRgwQGVlZdq5c6cWLFigHTt2qLy8XN9995169uypBx98UPfddx+jQ7UIgQgAgEugV69eatmypTZt2qTBgwdr9uzZxrpPPvlEv/zyi5o2barw8HDjtHtGh2oPzjIDAOAiOJ1OnTp1SoMHD9bXX3+tgwcP6rvvvtP8+fO1Z88eSVJkZKQGDx6sXbt2aefOncZrCUO1ByNEAABcIs8884y+//57nTp1St9//71atGihHj16KCIiQr6+vgoKCtLOnTuNaw+h9iAQAQBwifz6LLMPP/xQb775pr7//ntVVlaqoKBAf/rTn/TOO+9Uq0PtQCACAOAySk9PV3p6utq0aaPu3bvLz8+PydS1EIEIAABYHuN1AADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEACzvuuuu09y5cy/pNn/88UfZbDZlZWVd0u0CuDwIRAAAwPIIRABqvcrKSs2cOVM33HCD3Nzc1LJlSz333HOSpF27dqlXr15yd3eXj4+PRo0apeLiYuO1Dz74oO6++2698MILat68uXx8fPTII4+ovLxckhQREaEDBw7o8ccfl81mk81mkyT9/PPPuvfee9WiRQt5eHgoJCRE//rXv867r6CgIElS586dZbPZFBERcbl/TAAuAoEIQK03ZcoUzZw5U0899ZT27t2rN998U35+fjp58qT69eunxo0ba/v27XrnnXe0YcMGPfroo9Vev3HjRn3//ffauHGjli1bpqVLl2rp0qWSpHfffVctWrTQjBkzlJOTo5ycHEnSqVOnFBoaqtWrV2v37t0aNWqUYmNjtXXr1t/tS5K2bdsmSdqwYYNycnL07rvvXoGfFIAL5gSAWqyoqMjp5ubmXLJkSY11ixcvdjZu3NhZXFxsLPvoo4+c9erVc+bm5jqdTqdz2LBhzlatWjlPnz5t1AwePNg5dOhQ43mrVq2ciYmJv9vLHXfc4ZwwYcLv9uV0Op379+93SnLu3LnzfHYTgMkYIQJQq2VnZ6u0tFSRkZHnXNexY0d5enoay2655RZVVlbq66+/Npa1b99eLi4uxvPmzZsrLy/vN9+3oqJCzz33nG6++Wb5+Pjommuu0bp163Tw4MHf7QvA1cfV7AYA4Le4u7v/13VOp9OY83O2Xy+vX79+jXWVlZW/+b4vvviiEhMTNXfuXIWEhMjT01NxcXEqKyv73b4AXH0YIQJQqwUHB8vd3V2ffPJJjXXt2rVTVlaWTpw4YSzbvHmz6tWrpxtvvPG836NBgwaqqKiotuyzzz7TgAEDdP/996tjx45q3bq1vv322/Pqq2qbkmpsF0DtRCACUKs1bNhQkydP1qRJk/TGG2/o+++/V2Zmpl599VXdd999atiwoYYNG6bdu3dr48aNGjt2rGJjY43Jzefjuuuu06effqqffvpJx44dkyTdcMMNWr9+vTIyMpSdna3Ro0crNzf3vPqSJF9fX7m7uys1NVVHjx6Vw+G4tD8YAJcUgQhArffUU09pwoQJevrpp9W2bVsNHTpUeXl58vDw0Nq1a/XLL7+oa9eu+utf/6rIyEglJSX9oe3PmDFDP/74o66//no1a9bMeM8//elPuv322xURESF/f3/dfffd59WXJLm6uurll1/WokWLFBAQoAEDBlySnwWAy8PmdDqdZjcBAABgJkaIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5f1/uHN5zCtW+qkAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Bar plots for categorical columns\n",
+ "for i in range (len(categorical_cols)):\n",
+ " plt.figure()\n",
+ " sns.countplot(x=data[categorical_cols[i]], data=data)\n",
+ " plt.xticks(rotation=65) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "dd840803",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "age 171\n",
+ "balance 1055\n",
+ "day 0\n",
+ "duration 636\n",
+ "campaign 601\n",
+ "pdays 2750\n",
+ "previous 1258\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Check for outliers\n",
+ "outliers = outliers_check(data, numeric_cols)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f49629db",
+ "metadata": {},
+ "source": [
+ "# Correlation and Dimensionality Reduction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "dc3d0c9c",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\amych\\AppData\\Local\\Temp\\ipykernel_1748\\2627137660.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+ " data.corr()\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>age</th>\n",
+ " <th>balance</th>\n",
+ " <th>day</th>\n",
+ " <th>duration</th>\n",
+ " <th>campaign</th>\n",
+ " <th>pdays</th>\n",
+ " <th>previous</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>age</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.112300</td>\n",
+ " <td>-0.000762</td>\n",
+ " <td>0.000189</td>\n",
+ " <td>-0.005278</td>\n",
+ " <td>0.002774</td>\n",
+ " <td>0.020169</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>balance</th>\n",
+ " <td>0.112300</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.010467</td>\n",
+ " <td>0.022436</td>\n",
+ " <td>-0.013894</td>\n",
+ " <td>0.017411</td>\n",
+ " <td>0.030805</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>day</th>\n",
+ " <td>-0.000762</td>\n",
+ " <td>0.010467</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.018511</td>\n",
+ " <td>0.137007</td>\n",
+ " <td>-0.077232</td>\n",
+ " <td>-0.058981</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>duration</th>\n",
+ " <td>0.000189</td>\n",
+ " <td>0.022436</td>\n",
+ " <td>-0.018511</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.041557</td>\n",
+ " <td>-0.027392</td>\n",
+ " <td>-0.026716</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>campaign</th>\n",
+ " <td>-0.005278</td>\n",
+ " <td>-0.013894</td>\n",
+ " <td>0.137007</td>\n",
+ " <td>-0.041557</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.102726</td>\n",
+ " <td>-0.049699</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>pdays</th>\n",
+ " <td>0.002774</td>\n",
+ " <td>0.017411</td>\n",
+ " <td>-0.077232</td>\n",
+ " <td>-0.027392</td>\n",
+ " <td>-0.102726</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.507272</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>previous</th>\n",
+ " <td>0.020169</td>\n",
+ " <td>0.030805</td>\n",
+ " <td>-0.058981</td>\n",
+ " <td>-0.026716</td>\n",
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+ " <td>0.507272</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age balance day duration campaign pdays previous\n",
+ "age 1.000000 0.112300 -0.000762 0.000189 -0.005278 0.002774 0.020169\n",
+ "balance 0.112300 1.000000 0.010467 0.022436 -0.013894 0.017411 0.030805\n",
+ "day -0.000762 0.010467 1.000000 -0.018511 0.137007 -0.077232 -0.058981\n",
+ "duration 0.000189 0.022436 -0.018511 1.000000 -0.041557 -0.027392 -0.026716\n",
+ "campaign -0.005278 -0.013894 0.137007 -0.041557 1.000000 -0.102726 -0.049699\n",
+ "pdays 0.002774 0.017411 -0.077232 -0.027392 -0.102726 1.000000 0.507272\n",
+ "previous 0.020169 0.030805 -0.058981 -0.026716 -0.049699 0.507272 1.000000"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.corr()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35862085",
+ "metadata": {},
+ "source": [
+ "# One Hot Encoding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "10caf523",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'data_cleaned' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[16], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#One hot encoding\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m data_encoded \u001b[38;5;241m=\u001b[39m oneHotEncoding(data_cleaned, categorical_cols)\n\u001b[0;32m 5\u001b[0m data_encoded\u001b[38;5;241m.\u001b[39mhead()\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'data_cleaned' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "#One hot encoding\n",
+ "\n",
+ "data_encoded = oneHotEncoding(data_cleaned, categorical_cols)\n",
+ "\n",
+ "data_encoded.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "706be55f",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(58592, 89)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_encoded.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "4560bf4e",
+ "metadata": {
+ "scrolled": true
+ },
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+ {
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>policy_tenure</th>\n",
+ " <th>age_of_car</th>\n",
+ " <th>age_of_policyholder</th>\n",
+ " <th>population_density</th>\n",
+ " <th>make</th>\n",
+ " <th>airbags</th>\n",
+ " <th>displacement</th>\n",
+ " <th>cylinder</th>\n",
+ " <th>gear_box</th>\n",
+ " <th>turning_radius</th>\n",
+ " <th>length</th>\n",
+ " <th>width</th>\n",
+ " <th>height</th>\n",
+ " <th>gross_weight</th>\n",
+ " <th>ncap_rating</th>\n",
+ " <th>is_claim</th>\n",
+ " <th>max_torque_nm</th>\n",
+ " <th>max_torque_rpm</th>\n",
+ " <th>max_power_bhp</th>\n",
+ " <th>max_power_rpm</th>\n",
+ " <th>area_cluster_C10</th>\n",
+ " <th>area_cluster_C11</th>\n",
+ " <th>area_cluster_C12</th>\n",
+ " <th>area_cluster_C13</th>\n",
+ " <th>area_cluster_C14</th>\n",
+ " <th>area_cluster_C15</th>\n",
+ " <th>area_cluster_C16</th>\n",
+ " <th>area_cluster_C17</th>\n",
+ " <th>area_cluster_C18</th>\n",
+ " <th>area_cluster_C19</th>\n",
+ " <th>area_cluster_C2</th>\n",
+ " <th>area_cluster_C20</th>\n",
+ " <th>area_cluster_C21</th>\n",
+ " <th>area_cluster_C22</th>\n",
+ " <th>area_cluster_C3</th>\n",
+ " <th>area_cluster_C4</th>\n",
+ " <th>area_cluster_C5</th>\n",
+ " <th>area_cluster_C6</th>\n",
+ " <th>area_cluster_C7</th>\n",
+ " <th>area_cluster_C8</th>\n",
+ " <th>area_cluster_C9</th>\n",
+ " <th>segment_B1</th>\n",
+ " <th>segment_B2</th>\n",
+ " <th>segment_C1</th>\n",
+ " <th>segment_C2</th>\n",
+ " <th>segment_Utility</th>\n",
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+ " <th>model_M8</th>\n",
+ " <th>model_M9</th>\n",
+ " <th>fuel_type_Diesel</th>\n",
+ " <th>fuel_type_Petrol</th>\n",
+ " <th>engine_type_1.2 L K Series Engine</th>\n",
+ " <th>engine_type_1.2 L K12N Dualjet</th>\n",
+ " <th>engine_type_1.5 L U2 CRDi</th>\n",
+ " <th>engine_type_1.5 Turbocharged Revotorq</th>\n",
+ " <th>engine_type_1.5 Turbocharged Revotron</th>\n",
+ " <th>engine_type_F8D Petrol Engine</th>\n",
+ " <th>engine_type_G12B</th>\n",
+ " <th>engine_type_K Series Dual jet</th>\n",
+ " <th>engine_type_K10C</th>\n",
+ " <th>engine_type_i-DTEC</th>\n",
+ " <th>is_esc_Yes</th>\n",
+ " <th>is_adjustable_steering_Yes</th>\n",
+ " <th>is_tpms_Yes</th>\n",
+ " <th>is_parking_sensors_Yes</th>\n",
+ " <th>is_parking_camera_Yes</th>\n",
+ " <th>rear_brakes_type_Drum</th>\n",
+ " <th>transmission_type_Manual</th>\n",
+ " <th>steering_type_Manual</th>\n",
+ " <th>steering_type_Power</th>\n",
+ " <th>is_front_fog_lights_Yes</th>\n",
+ " <th>is_rear_window_wiper_Yes</th>\n",
+ " <th>is_rear_window_washer_Yes</th>\n",
+ " <th>is_rear_window_defogger_Yes</th>\n",
+ " <th>is_brake_assist_Yes</th>\n",
+ " <th>is_power_door_locks_Yes</th>\n",
+ " <th>is_central_locking_Yes</th>\n",
+ " <th>is_power_steering_Yes</th>\n",
+ " <th>is_driver_seat_height_adjustable_Yes</th>\n",
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+ " <th>is_ecw_Yes</th>\n",
+ " <th>is_speed_alert_Yes</th>\n",
+ " </tr>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17050</th>\n",
+ " <td>0.036614</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.432692</td>\n",
+ " <td>17804</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>796</td>\n",
+ " <td>3</td>\n",
+ " <td>5</td>\n",
+ " <td>4.6</td>\n",
+ " <td>3445</td>\n",
+ " <td>1515</td>\n",
+ " <td>1475</td>\n",
+ " <td>1185</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>60.0</td>\n",
+ " <td>3500</td>\n",
+ " <td>40.36</td>\n",
+ " <td>6000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35146</th>\n",
+ " <td>1.041438</td>\n",
+ " <td>0.04</td>\n",
+ " <td>0.576923</td>\n",
+ " <td>8794</td>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>1197</td>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>4.8</td>\n",
+ " <td>3845</td>\n",
+ " <td>1735</td>\n",
+ " <td>1530</td>\n",
+ " <td>1335</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>113.0</td>\n",
+ " <td>4400</td>\n",
+ " <td>88.50</td>\n",
+ " <td>6000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " policy_tenure age_of_car age_of_policyholder population_density \\\n",
+ "29918 0.285690 0.05 0.509615 4076 \n",
+ "5034 0.122252 0.05 0.403846 8794 \n",
+ "38100 0.213020 0.03 0.365385 4076 \n",
+ "17050 0.036614 0.00 0.432692 17804 \n",
+ "35146 1.041438 0.04 0.576923 8794 \n",
+ "\n",
+ " make airbags displacement cylinder gear_box turning_radius \\\n",
+ "29918 5 2 1498 4 5 4.9 \n",
+ "5034 1 2 1197 4 5 4.8 \n",
+ "38100 3 6 1493 4 6 5.2 \n",
+ "17050 1 2 796 3 5 4.6 \n",
+ "35146 1 2 1197 4 5 4.8 \n",
+ "\n",
+ " length width height gross_weight ncap_rating is_claim \\\n",
+ "29918 3995 1695 1501 1051 4 0 \n",
+ "5034 3845 1735 1530 1335 2 0 \n",
+ "38100 4300 1790 1635 1720 3 0 \n",
+ "17050 3445 1515 1475 1185 0 0 \n",
+ "35146 3845 1735 1530 1335 2 0 \n",
+ "\n",
+ " max_torque_nm max_torque_rpm max_power_bhp max_power_rpm \\\n",
+ "29918 200.0 1750 97.89 3600 \n",
+ "5034 113.0 4400 88.50 6000 \n",
+ "38100 250.0 2750 113.45 4000 \n",
+ "17050 60.0 3500 40.36 6000 \n",
+ "35146 113.0 4400 88.50 6000 \n",
+ "\n",
+ " area_cluster_C10 area_cluster_C11 area_cluster_C12 area_cluster_C13 \\\n",
+ "29918 0.0 0.0 0.0 0.0 \n",
+ "5034 0.0 0.0 0.0 0.0 \n",
+ "38100 0.0 0.0 0.0 0.0 \n",
+ "17050 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " area_cluster_C14 area_cluster_C15 area_cluster_C16 area_cluster_C17 \\\n",
+ "29918 0.0 0.0 0.0 0.0 \n",
+ "5034 0.0 0.0 0.0 0.0 \n",
+ "38100 0.0 0.0 0.0 0.0 \n",
+ "17050 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " area_cluster_C18 area_cluster_C19 area_cluster_C2 area_cluster_C20 \\\n",
+ "29918 0.0 0.0 0.0 0.0 \n",
+ "5034 0.0 0.0 0.0 0.0 \n",
+ "38100 0.0 0.0 0.0 0.0 \n",
+ "17050 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " area_cluster_C21 area_cluster_C22 area_cluster_C3 area_cluster_C4 \\\n",
+ "29918 0.0 0.0 1.0 0.0 \n",
+ "5034 0.0 0.0 0.0 0.0 \n",
+ "38100 0.0 0.0 1.0 0.0 \n",
+ "17050 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " area_cluster_C5 area_cluster_C6 area_cluster_C7 area_cluster_C8 \\\n",
+ "29918 0.0 0.0 0.0 0.0 \n",
+ "5034 0.0 0.0 0.0 1.0 \n",
+ "38100 0.0 0.0 0.0 0.0 \n",
+ "17050 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 0.0 1.0 \n",
+ "\n",
+ " area_cluster_C9 segment_B1 segment_B2 segment_C1 segment_C2 \\\n",
+ "29918 0.0 0.0 0.0 1.0 0.0 \n",
+ "5034 0.0 0.0 1.0 0.0 0.0 \n",
+ "38100 0.0 0.0 0.0 0.0 1.0 \n",
+ "17050 1.0 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 1.0 0.0 0.0 \n",
+ "\n",
+ " segment_Utility model_M10 model_M11 model_M2 model_M3 model_M4 \\\n",
+ "29918 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "5034 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "38100 0.0 0.0 0.0 0.0 0.0 1.0 \n",
+ "17050 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " model_M5 model_M6 model_M7 model_M8 model_M9 fuel_type_Diesel \\\n",
+ "29918 0.0 0.0 0.0 0.0 1.0 1.0 \n",
+ "5034 0.0 1.0 0.0 0.0 0.0 0.0 \n",
+ "38100 0.0 0.0 0.0 0.0 0.0 1.0 \n",
+ "17050 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "35146 0.0 1.0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ " fuel_type_Petrol engine_type_1.2 L K Series Engine \\\n",
+ "29918 0.0 0.0 \n",
+ "5034 1.0 0.0 \n",
+ "38100 0.0 0.0 \n",
+ "17050 0.0 0.0 \n",
+ "35146 1.0 0.0 \n",
+ "\n",
+ " engine_type_1.2 L K12N Dualjet engine_type_1.5 L U2 CRDi \\\n",
+ "29918 0.0 0.0 \n",
+ "5034 0.0 0.0 \n",
+ "38100 0.0 1.0 \n",
+ "17050 0.0 0.0 \n",
+ "35146 0.0 0.0 \n",
+ "\n",
+ " engine_type_1.5 Turbocharged Revotorq \\\n",
+ "29918 0.0 \n",
+ "5034 0.0 \n",
+ "38100 0.0 \n",
+ "17050 0.0 \n",
+ "35146 0.0 \n",
+ "\n",
+ " engine_type_1.5 Turbocharged Revotron engine_type_F8D Petrol Engine \\\n",
+ "29918 0.0 0.0 \n",
+ "5034 0.0 0.0 \n",
+ "38100 0.0 0.0 \n",
+ "17050 0.0 1.0 \n",
+ "35146 0.0 0.0 \n",
+ "\n",
+ " engine_type_G12B engine_type_K Series Dual jet engine_type_K10C \\\n",
+ "29918 0.0 0.0 0.0 \n",
+ "5034 0.0 1.0 0.0 \n",
+ "38100 0.0 0.0 0.0 \n",
+ "17050 0.0 0.0 0.0 \n",
+ "35146 0.0 1.0 0.0 \n",
+ "\n",
+ " engine_type_i-DTEC is_esc_Yes is_adjustable_steering_Yes \\\n",
+ "29918 1.0 0.0 1.0 \n",
+ "5034 0.0 0.0 1.0 \n",
+ "38100 0.0 1.0 1.0 \n",
+ "17050 0.0 0.0 0.0 \n",
+ "35146 0.0 0.0 1.0 \n",
+ "\n",
+ " is_tpms_Yes is_parking_sensors_Yes is_parking_camera_Yes \\\n",
+ "29918 0.0 1.0 1.0 \n",
+ "5034 0.0 1.0 0.0 \n",
+ "38100 1.0 1.0 1.0 \n",
+ "17050 0.0 1.0 0.0 \n",
+ "35146 0.0 1.0 0.0 \n",
+ "\n",
+ " rear_brakes_type_Drum transmission_type_Manual steering_type_Manual \\\n",
+ "29918 1.0 1.0 0.0 \n",
+ "5034 1.0 1.0 0.0 \n",
+ "38100 0.0 0.0 0.0 \n",
+ "17050 1.0 1.0 0.0 \n",
+ "35146 1.0 1.0 0.0 \n",
+ "\n",
+ " steering_type_Power is_front_fog_lights_Yes is_rear_window_wiper_Yes \\\n",
+ "29918 0.0 1.0 0.0 \n",
+ "5034 0.0 1.0 0.0 \n",
+ "38100 1.0 1.0 1.0 \n",
+ "17050 1.0 0.0 0.0 \n",
+ "35146 0.0 1.0 0.0 \n",
+ "\n",
+ " is_rear_window_washer_Yes is_rear_window_defogger_Yes \\\n",
+ "29918 0.0 1.0 \n",
+ "5034 0.0 0.0 \n",
+ "38100 1.0 1.0 \n",
+ "17050 0.0 0.0 \n",
+ "35146 0.0 0.0 \n",
+ "\n",
+ " is_brake_assist_Yes is_power_door_locks_Yes is_central_locking_Yes \\\n",
+ "29918 0.0 1.0 1.0 \n",
+ "5034 1.0 1.0 1.0 \n",
+ "38100 1.0 1.0 1.0 \n",
+ "17050 0.0 0.0 0.0 \n",
+ "35146 1.0 1.0 1.0 \n",
+ "\n",
+ " is_power_steering_Yes is_driver_seat_height_adjustable_Yes \\\n",
+ "29918 1.0 1.0 \n",
+ "5034 1.0 1.0 \n",
+ "38100 1.0 1.0 \n",
+ "17050 1.0 0.0 \n",
+ "35146 1.0 1.0 \n",
+ "\n",
+ " is_day_night_rear_view_mirror_Yes is_ecw_Yes is_speed_alert_Yes \n",
+ "29918 1.0 1.0 1.0 \n",
+ "5034 1.0 1.0 1.0 \n",
+ "38100 0.0 1.0 1.0 \n",
+ "17050 0.0 0.0 1.0 \n",
+ "35146 1.0 1.0 1.0 "
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Randomise the dataset\n",
+ "\n",
+ "data_sampled = data_encoded.sample(frac=1, random_state=42)\n",
+ "\n",
+ "#All features except the target are assigned to 'X'\n",
+ "X = data_sampled.drop(\"is_claim\", axis=1)\n",
+ "\n",
+ "#Target feature is assigned to 'Y'\n",
+ "Y = data_sampled[\"is_claim\"]\n",
+ "\n",
+ "data_sampled.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca7697d9",
+ "metadata": {},
+ "source": [
+ "# Dataset Rebalancing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1389cec9",
+ "metadata": {},
+ "source": [
+ "Undersampling and Oversampling sampling methods were used for modelling. Results for both methods will be compared to the result of modelling without any sampling method.\n",
+ "\n",
+ "Rebalancing is done on the training set only."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "ad90f3bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#split into train and test\n",
+ "\n",
+ "xtrain, xtest, ytrain, ytest = train_test_split(X,Y, test_size=0.3, random_state=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "90c10eaf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(5106, 88)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Undersampling\n",
+ "\n",
+ "undersampling = RandomUnderSampler(random_state=42)\n",
+ "\n",
+ "xtrain_undersampled, ytrain_undersampled = undersampling.fit_resample(xtrain, ytrain)\n",
+ "\n",
+ "xtrain_undersampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "7f76dc3e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(76922, 88)"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Oversampling\n",
+ "\n",
+ "oversampling = RandomOverSampler(random_state=42)\n",
+ "\n",
+ "xtrain_oversampled, ytrain_oversampled = oversampling.fit_resample(xtrain, ytrain)\n",
+ "\n",
+ "xtrain_oversampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "33486b2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Undersampled:\n",
+ " 0 2553\n",
+ "1 2553\n",
+ "Name: is_claim, dtype: int64\n",
+ "\n",
+ "Oversampled:\n",
+ " 0 38461\n",
+ "1 38461\n",
+ "Name: is_claim, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Target column is now balanced\n",
+ "\n",
+ "print(\"Undersampled:\\n\", ytrain_undersampled.value_counts())\n",
+ "\n",
+ "print(\"\\nOversampled:\\n\", ytrain_oversampled.value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35a04fc0",
+ "metadata": {},
+ "source": [
+ "# Dataset Scaling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "667b8bfb",
+ "metadata": {},
+ "source": [
+ "Both oversampled and undersampled datasets are scaled and PCA will be applied to them"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "42cfe508",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(5106, 88)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Scaling the data - undersampled dataset\n",
+ "\n",
+ "scaler_undersampled = StandardScaler()\n",
+ "xtrain_scaled_undersampled = scaler_undersampled.fit_transform(xtrain_undersampled)\n",
+ "xtest_scaled_undersampled = scaler_undersampled.fit_transform(xtest)\n",
+ "\n",
+ "\n",
+ "xtrain_scaled_undersampled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "f515c607",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(76922, 88)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Scaling the data - oversampled dataset\n",
+ "\n",
+ "scaler_oversampled = StandardScaler()\n",
+ "xtrain_scaled_oversampled = scaler_oversampled.fit_transform(xtrain_oversampled)\n",
+ "xtest_scaled_oversampled = scaler_oversampled.fit_transform(xtest)\n",
+ "\n",
+ "\n",
+ "xtrain_scaled_oversampled.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05988265",
+ "metadata": {},
+ "source": [
+ "# Dimensionality Reduction Using Principal Component Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "cfd07695",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#PCA - oversampled dataset\n",
+ "\n",
+ "pca_undersampled = PCA(84)\n",
+ "\n",
+ "xtrain_pca_undersampled = pca_undersampled.fit_transform(xtrain_scaled_undersampled)\n",
+ "\n",
+ "xtest_pca_undersampled = pca_undersampled.fit_transform(xtest_scaled_undersampled)\n",
+ "\n",
+ "#pca.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "98a3d312",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#PCA - oversampled dataset\n",
+ "\n",
+ "pca_oversampled = PCA(84)\n",
+ "\n",
+ "xtrain_pca_oversampled = pca_oversampled.fit_transform(xtrain_scaled_oversampled)\n",
+ "\n",
+ "xtest_pca_oversampled = pca_oversampled.fit_transform(xtest_scaled_oversampled)\n",
+ "\n",
+ "#pca.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "722202d4",
+ "metadata": {},
+ "source": [
+ "# Modelling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b0d35a9",
+ "metadata": {},
+ "source": [
+ "Modelling will be done on the dataset after scaling, and the dataset after PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "5d824f45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "LogisticRegression()"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Logistic regresison model - undersampled dataset\n",
+ "\n",
+ "reg_undersampled = LogisticRegression()\n",
+ "reg_undersampled.fit(xtrain_pca_undersampled, ytrain_undersampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "9ec369bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "LogisticRegression()"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Logistic regresison model - oversampled dataset\n",
+ "\n",
+ "reg_oversampled = LogisticRegression()\n",
+ "reg_oversampled.fit(xtrain_pca_oversampled, ytrain_oversampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "97492efd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "LogisticRegression()"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Logistic regresison model - no sampling, scaling or PCA done on this dataset\n",
+ "\n",
+ "reg = LogisticRegression()\n",
+ "reg.fit(xtrain, ytrain)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "c21986e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Predictions\n",
+ "\n",
+ "predictions_undersampled = reg_undersampled.predict(xtest_pca_undersampled)\n",
+ "\n",
+ "predictions_oversampled = reg_oversampled.predict(xtest_pca_oversampled)\n",
+ "\n",
+ "predictions = reg.predict(xtest)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5de8b9a5",
+ "metadata": {},
+ "source": [
+ "# Evaluations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "31f0302e",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following are results of the logistic regression model and different sampling methods\n",
+ "\n",
+ "Undersampling: 0.5204232563431562\n",
+ "Oversampling: 0.5033564683126636\n",
+ "No sampling: 0.9320172943452042\n"
+ ]
+ }
+ ],
+ "source": [
+ "accuracy_undersampled = accuracy_score(ytest, predictions_undersampled)\n",
+ "accuracy_oversampled = accuracy_score(ytest, predictions_oversampled)\n",
+ "accuracy = accuracy_score(ytest, predictions)\n",
+ "\n",
+ "print(\"The following are results of the logistic regression model and different sampling methods\\n\")\n",
+ "print(\"Undersampling:\", accuracy_undersampled)\n",
+ "print(\"Oversampling:\", accuracy_oversampled)\n",
+ "print(\"No sampling:\", accuracy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "3ffabfde",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[8454 7929]\n",
+ " [ 501 694]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(confusion_matrix(ytest, predictions_undersampled))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "a4da96da",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[8161 8222]\n",
+ " [ 508 687]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(confusion_matrix(ytest, predictions_oversampled))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "6915814c",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[16383 0]\n",
+ " [ 1195 0]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(confusion_matrix(ytest, predictions))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}